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arXiv:2505.16325v1 [cs.CL] 22 May 2025CLEAR: A Clinically-Grounded Tabular Framework for Radiology Report Evaluation Yuyang Jiang1, Chacha Chen1, Shengyuan Wang2, Feng Li1, Zecong Tang3, Benjamin M. Mervak4,Lydia Chelala1,Christopher M Straus1,Reve Chahine4, Samuel G. Armato III1*,Chenhao Tan1* 1University of Chicago2Tsinghua University 3Zhejiang University4University of Michigan Abstract Existing metrics often lack the granularity and interpretability to capture nuanced clinical dif- ferences between candidate and ground-truth radiology reports, resulting in suboptimal eval- uation. We introduce a Clinically-grounded tabular framework with Expert-curated labels andAttribute-level comparison for Radiology report evaluation ( CLEAR ). CLEAR not only examines whether a report can accurately iden- tify the presence or absence of medical condi- tions, but also assesses whether it can precisely describe each positively identified condition across five key attributes: first occurrence , change ,severity ,descriptive location , and recommendation . Compared to prior works, CLEAR’s multi-dimensional, attribute- level outputs enable a more comprehensive and clinically interpretable evaluation of re- port quality. Additionally, to measure the clinical alignment of CLEAR , we collaborate with five board-certified radiologists to develop CLEAR-Bench , a dataset of 100 chest X-ray reports from MIMIC-CXR, annotated across 6 curated attributes and 13 CheXpert condi- tions. Our experiments show that CLEAR achieves high accuracy in extracting clinical attributes and provides automated metrics that are strongly aligned with clinical judgment. 1 Introduction Evaluation is becoming increasingly challenging in the era of large language models (LLMs). While models continue to hill-climb on benchmarks rapidly (Maslej et al., 2025; OpenAI, 2025; An- thropic, 2025; Tu et al., 2025; McDuff et al., 2025), it remains unclear whether these reported metrics match task-specific needs (Ganguli et al., 2023; Rauh et al., 2024; Bedi et al., 2025). In the context of radiology, the pursuit of generalist foundation models achieves promising progress (Bannur et al., 2024; Zambrano Chaves et al., 2025), but do these *Co-senior authorship.“appealing” automated metrics truly capture clini- cally aligned qualities (Paschali et al., 2025)? In the existing literature, three main types of met- rics have been proposed to assess the quality of gen- erated radiology reports, as illustrated in Figure 1: (i)Lexical metrics measure surface-level similar- ity between the generated and ground-truth reports (Papineni et al., 2002; Lin, 2004; Zhang et al., 2020). While straightforward and easy to com- pute, they struggle to capture nuanced semantics and domain-specific terminology, leading to poor sensitivity to clinically significant errors. (ii) Clin- ical efficacy metrics evaluate the correctness of medical entities and their relationships (Jain et al., 2021; Yu et al., 2023b; Zhao et al., 2024), typically through structured extraction-based comparisons. Although more clinically informed than lexical met- rics, they lack the resolution to assess fine-grained attributes such as severity, temporal progression, or treatment recommendations. (iii) LLM-based metrics (Ostmeier et al., 2024; Huang et al., 2024; Zambrano Chaves et al., 2025) represent the latest direction, often leveraging the pipeline of LLM- as-a-Judge (Zheng et al., 2023) with pre-defined taxonomies such as the six error categories from ReXVal dataset (Yu et al., 2023a). While getting closer to expert judgment compared with previous two types, these methods may still lack compre- hensive structured attribution | https://arxiv.org/abs/2505.16325v1 |
and condition-level interpretability. Therefore, to address the limitations of existing metrics, we introduce CLEAR (Section 2), the first clinically-grounded attribute-level evaluation framework that leverages LLMs to map free-text ra- diology reports to a structured tabular format. Com- pared to prior work, CLEAR transforms the coarse, single-dimensional taxonomy into a fine-grained, multidimensional structure. Our design not only enables more comprehensive comparisons between candidate and ground-truth reports, but also pro- vides interpretable outputs to assess report quality 1 LLM-based Metrics Lack a structure and does not account for hierarchical or multi-dimensional relationships among errors.FineRadScore(‘24), GREEN (‘24), CheXprompt(‘25)Based on six error categories (Yu et al., 2023)1.False prediction of finding;2.Omission of finding;3.Incorrect location/position of finding;4.Incorrect severity of finding;5.Mention of the comparison that is absent from the reference impression;6.Omission of comparison describing a change from a previous study.CLEAR (ours)Tabular Evaluation (13 conditions X 6 attributes)Lexical MetricsBLEU (’02), ROUGE-L (’04), BERTScore(’20) There is evidence of pleural effusion. Candidate Report 2 Noevidence of pleural effusion. Candidate Report 1 Without clear evidence of pleural effusion.Clinical Efficacy Metrics Lack the granularity to assess attributes beyond entities and relations.CheXbertF1 (‘20), RadGraphF1 (‘21), RaTEScore(‘24)There is a leftpleural effusion, newsince the prior exam, associated with atelectasisof the left lowerlobe. Recommend urgent thoracentesis.Candidate ReportA right pleural effusion is present likely chronic, with associated atelectasis in the lower lobe. No intervention is recommended.AbnormalityAbnormalityAnatomyAbnormalityAbnormalityAnatomyGT Report GT Report Provides fine-grained, clinically grounded analysis through a structured, interpretable tabular format that enables easy comparison between reports.✗Fail in capturing nuanced semantics.✗✗ Figure 1: A comparison of existing metrics with CLEAR. Yellow highlights indicate the main evaluation mechanism for each type of metric. Red underlining marks an erroneous term in the candidate report, in contrast to the black underlined term in the ground-truth report, which the designed metric fails to evaluate. at the level of condition-attribute pairs. Given the strong adaptability of LLMs across diverse lan- guage tasks, they serve as an ideal unified model to operationalize our proposed framework. Specifically, CLEAR begins with the Label Ex- traction Module (Section 2.1), which evaluates whether the candidate report can precisely identify the presence or absence of specific medical condi- tions. To ensure robust performance across model scales, we enhance this module using high-quality, expert-curated labels. Next, for each correctly iden- tified positive condition, the Description Extrac- tion Module (Section 2.2) assesses whether the candidate report can accurately describe the condi- tion. Jointly established with one research radiol- ogist and reviewed by one clinical radiologist, we define five commonly used attributes in a radiology report ( first occurrence ,change ,severity , descriptive location , and recommendation ), enabling the first systematic evaluation of these critical facets. Finally, the Scoring Module (Sec- tion 2.3) compiles and outputs metric scores for each attribute. We carefully design automated mea- surements based on the output type from previous modules: accuracy metrics aim at exact matches for single-label outputs while similarity metrics focus on contextual relevance for multi-phrasing outputs. Additionally, since no existing datasets (Tian et al., 2023; Yu et al., 2023a; Rao et al., 2025) are compatible with CLEAR, we work closely with radiologists to create CLEAR-Bench | https://arxiv.org/abs/2505.16325v1 |
(Section 3), an expert-curated, attribute-level dataset to as- sess clinical alignment. CLEAR-Bench consists of 100 studies randomly sampled from MIMIC-CXR- JPG test and validation sets (Johnson et al., 2019, 2024). Each study is annotated and reviewed by at least two radiologists across 6 report attributesand 13 CheXpert conditions1(Irvin et al., 2019). CLEAR-Bench includes two components: (i) Ex- pert ensemble labels includes ground-truth labels forpresence attribute of each condition. These labels are constructed via majority voting among three radiologists, followed by one round of con- sensus discussion. (ii) Expert curated attributes contains the remaining five report attributes for each condition positively identified in the ensem- ble labels. These attributes are first generated by LLMs, then independently curated by two radiolo- gists, and finalized through one round of discussion and resolution. Additionally, during the curation process, we collect expert Likert scores for each model output, contributing to the assessment of how well proposed automated metrics align with clinical judgment. Finally, we evaluate each component of CLEAR using the CLEAR-Bench. Our experimental re- sults (Section 4) show that: (i) the Label Extraction Module achieves high accuracy compared to expert ensemble labels and significantly outperforms exist- ing labelers across all metrics; (ii) the Description Extraction Module can accurately extract attribute- level information according to clinical assessment; (iii) our proposed automated metrics serve as effec- tive proxies for expert scoring. 2 CLEAR Framework We introduce the CLEAR framework, a hierar- chical and fine-grained system for evaluating the clinical accuracy of radiology reports. CLEAR addresses both high-level diagnostic correctness and the descriptive quality of positive findings. As 1Atelectasis, Cardiomegaly, Consolidation, Edema, En- larged Cardiomediastinum, Fracture, Lung Lesion, Lung Opacity, Pleural Effusion, Pleural Other, Pneumonia, Pneu- mothorax, and Support Devices. 2 Lung OpacityAtelectasisPneumothoraxConsolidationPneumoniaSupport DevicesStage 1: Label Extraction Module Lung Opacity ✅Atelectasis ✅Pneumothorax ❌Consolidation ✅Pneumonia ❌Support Devices ❌Stage 2: Description Extraction Module Support DevicesPneumothoraxPneumoniaConsolidationAtelectasisLung Opacity ❌ ❌ ❌ ✅(Unclear) ✅(Positive) ✅(Positive)Presence———— ❌ ❌First Occurrence———— ❌ ❌Change———— ✅ ✅Severity————0.950.65DescriptiveLocation————0.90.9RecommendationStage 3: Scoring Module[Output] Multi-Metric (Attribute-Level) Presence(Accuracy)[Input] [Ground-truth Report]Human-Written Chest X-ray Report[Input] [Candidate Report]AI-Generated Chest X-ray ReportFINDINGS: Right internal jugular central line remains in place with tip in the distal superior vena cava. No pneumothorax identified. IMPRESSION: Persistent low lung volumes with patchy bibasilar opacities and a probable layering left effusion, suggestive of compressive atelectasis. Follow-up chest radiograph in 24–48 hours.FINDINGS: Small left apical pneumothorax is present.Bilateral lower lobe opacities and volume loss are noted. IMPRESSION: 1. Findings suggest early pneumonia and associated bibasilar atelectasis. 2. Small left pneumothorax, of uncertain significance. Repeat chest X-ray in 24 hours to monitor pneumothorax and opacities. AtelectasisLung OpacityPreviousPreviousFirst OccurrenceStableStableChangeN/AN/ASeverity[“patchy bibasilar opacities”, “layering left effusion”][“bibasilar compressive atelectasis”]Descriptive Location[“follow-up chest radiograph in 24–48 hours”][“follow-up chest radiograph in 24–48 hours”]RecommendationAtelectasisLung OpacityN/A ❌N/A ❌First OccurrenceN/A ❌N/A ❌ChangeN/A ✅N/A ✅Severity[“bibasilar atelectasis”][“bilateral lower lobe opacities”, “bibasilar atelectasis”]Descriptive Location[“repeat chest X-ray in 24 hours”][“repeat chest X-ray in 24 hours”]RecommendationFirst Occurrence (Accuracy)Change(Accuracy)Descriptive Location(Similarity) Recommendation(Similarity) Positive ConditionNegative ConditionUnclear ConditionPositive condition correctly identified by the candidate report ❌Incorrectly identified by the candidate report ✅Correctly identified by the candidate report*A dash (—) means that this attribute is not applicable for this condition.ConditionAttribute ConditionAttributeAttributeCondition Severity(Accuracy)Figure 2: CLEAR Framework. | https://arxiv.org/abs/2505.16325v1 |
Given a pair of ground-truth and candidate reports, we first assesses whether the candidate report can accurately identify a set of medical observations in the label extraction module . For each correctly identified positive condition, the description extraction module further evaluates the report’s ability to describe the condition across five attributes: first occurrence ,change ,severity ,descriptive location , andrecommendation . Finally, the scoring module compiles and outputs the evaluation metrics. shown in Figure 2, CLEAR includes three sequen- tial stages: label extraction, description extraction, and structured scoring. Specifically, given a ground-truth and a candi- date report pair, CLEAR first identifies whether the candidate correctly recognizes the presence or absence of specific medical conditions (Stage 1). It then examines, for each positively identified condition, whether the ground-truth and candidate reports are aligned across a set of expert-curated descriptive dimensions (Stage 2). Finally, it ag- gregates these evaluations into standardized, multi- dimensional metrics (Stage 3). 2.1 Stage 1: Label Extraction This stage determines the presence or absence of 13 pre-defined medical conditions in the candidate re- port, following the CheXpert structure (Irvin et al., 2019). Since accurately identifying and describing abnormalities is more clinically significant in ra- diology reporting, we exclude the “No Findings” label and focus on the remaining 13 conditions. Each condition is labeled as positive ,unclear , ornegative based on report content. While existing labelers like CheXbert (Smit et al., 2020) and CheXpert (Irvin et al., 2019) are available, our pilot analysis (see Table 2) showed that their performance was limited. Since labelextraction involves understanding and interpreting clinical narratives to assign structured labels, we hypothesized that LLMs could offer significant im- provements over existing approaches. In particular, LLMs can handle complex linguistic nuances, such as negation, uncertainty, and context-dependent phrasing, more effectively in free-form radiology reports. Base model variants and training strategies. We support three model scales: small (fine-tuned Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct), medium (Llama-3.3-70B-Instruct and Llama-3.1- 70B-Instruct), and large (GPT-4o). For medium and large models, we apply different prompting strategies, including zero-shot (Prompt 1) and five- shot. For small models, we perform full-parameter fine-tuning using our curated dataset. To avoid overfitting, we first conduct hyperparameter tuning through 5-fold cross-validation and a grid search over learning rate, gradient accumulation steps, and number of epochs, followed by re-training on the full dataset. Full implementation details are pro- vided in Appendix C. Expert-in-the-loop label curation. High-quality labeled data is essential for training our label ex- traction model. To build a gold training dataset, we implemented a multi-stage annotation refine- 3 Attribute Value Set NLP Task Metric Presence S1∈{"Positive", "Unclear", "Negative"} Cls (Prompt 1) Accuracy Temporal Assessment First Occurrence S2∈{"Previous", "Current", "N/A"} QA (Prompt 2) Accuracy Change S3∈{"Improving", "Stable", "Worsening", "Mixed", "N/A"} QA (Prompt 3) Accuracy Description Assessment Severity S4∈{"Severe", "Moderate", "Mild", "Mixed", "N/A"} QA (Prompt 4) Accuracy Descriptive Location S5={Entry 1, ..., Entry n} (e.g., Entry m= "left mid lung atelectasis")IE (Prompt 5) Similarity Treatment Assessment Recommendation S6={Entry 1, ..., Entry n} (e.g., Entry m= "recommend follow-up at 4 weeks")IE (Prompt 6) Similarity ∗Cls denotes “Classification,” QA denotes “Question Answering,” and IE denotes “Information | https://arxiv.org/abs/2505.16325v1 |
Extraction.” Table 1: An overview of our expert-curated fine-grained attributes in CLEAR. ment with expert in the loop. We began with the test set from MIMIC-CXR-JPG (Johnson et al., 2024), which includes a single radiologist’s annota- tions for 13 CheXpert conditions (Irvin et al., 2019). Each condition is originally labeled as positive , negative ,unmentioned , oruncertain . In initial discussions with a radiologist, we identified two major issues with the original annotations: labeling errors (e.g., conditions mentioned in the report but left unlabeled) and category ambiguity (e.g., vague distinctions between negative andunmentioned ). To address these, we used GPT-4o to pre-screen and re-label the reports, prompting it with the orig- inal MIMIC labeling guidelines. We then flagged cases with label mismatches between GPT-4o and the original annotations. We then asked an ex- pert to re-annotate the discrepancy cases. To re- duce the radiologist’s workload, reports with more than five mismatched condition labels are discarded from expert annotation, as such extensive disagree- ment often signals deeper interpretive ambiguities or quality issues in the original reports. While this introduces potential bias, we prioritized curating a high-quality subset over exhaustively correcting all samples. For the remaining reports, our col- laborating radiologist independently re-annotated only the discrepant conditions, reviewing the origi- nal report text without seeing prior labels. During human annotation process, we observed that the original labeling schema lacked sufficient granular- ity to reflect the nuanced certainty levels expressed in radiology. In discussion with our expert radiol- ogist, we expanded the label set to: {confidently present, likely present, neutral, likely absent, confidently absent} . In total, we cu- rated 550 studies, each with high-quality labels for all 13 conditions. For consistency with prior work and to simplify downstream modeling, we furthermerged all labels into three classes { positive, negative, unclear }. A detailed description of the annotation process and instructions are pro- vided in Appendix B. 2.2 Stage 2: Description Extraction Building on the condition labels from Stage 1, this module extracts fine-grained clinical features that capture essential descriptive information for accu- rate reporting. The primary motivation is to trans- form the narrative text of radiology reports into a comprehensive, structured tabular format that dis- tills all clinically significant attributes. In collabo- ration with two radiologists, we developed five clin- ically significant dimensions: first occurrence (whether the condition is newly observed), change (progression or improvement from prior studies), severity (the extent or intensity of the condi- tion), descriptive location (specific anatom- ical site), and recommendation (suggested follow- up actions). These expert-developed attributes were specifically designed to reflect the nuanced but es- sential information radiologists routinely document when interpreting chest X-rays. By extracting these attributes, our approach enables a more comprehen- sive evaluation beyond simple condition detection. Implementation details. We use prompt-based methods to extract each of the five attributes from free-text reports. Each attribute can be natu- rally framed as a standalone language understand- ing task. To operationalize this, we design cus- tom prompts tailored to the nature of each at- tribute: we use a Question Answering (QA) tem- plate to prompt the model | https://arxiv.org/abs/2505.16325v1 |
for first occurrence (Prompt 2), change (Prompt 3), and severity (Prompt 4), and an Information Extraction (IE) tem- plate for descriptive location (Prompt 5) and recommendation (Prompt 6). For QA tasks, the 4 model selects the best answer from multiple-choice options based on its understanding of the report. For IE tasks, it extracts relevant phrases guided by condition-specific example terminologies. Our prompt templates and terminology lists are summa- rized in Appendix D, and were reviewed by two radiologists. We use a single model to process all five prompt types, one prompt per query to extract each attribute from a given report. We evaluate two model scales: a smaller Llama-3.1-8B-Instruct and a larger GPT-4o from OpenAI. 2.3 Stage 3: Scoring and Metrics In this module, we process outputs from Stage 1 and Stage 2 into numeric metrics for each attribute. Given the i-th pair of ground-truth and candidate attribute sets, denote the attributes extracted from the ground-truth report as {S(i) j}6 j=1and from the candidate report as {ˆS(i) j}6 j=1. An overview of the attributes is provided in Table 1. For presence (S1,ˆS1), we evaluate the ac- curacy of identifying Positive and Negative conditions. We define a target class c∈ {Positive ,Negative }, treating all other labels as non-target. The corresponding binary F1 score, F1c, is computed for each target class, resulting a positive-F1 and negative-F1. We report these scores at three levels: micro average, Top-5 condi- tion average2, and across all 13 conditions. For first occurrence (S2,ˆS2),change (S3,ˆS3), and severity (S4,ˆS4), we assess the ex- act match between predictions and ground truth. Considering that these attributes are framed as multiple-choice questions in the prompt, exact match is a natural and appropriate metric. Accuracy is calculated as Acc. j=P i⊮[S(i) j=ˆS(i) j]P i1. We report accuracy at the micro level, as well as averaged across reports and the 13 conditions. For descriptive location (S5,ˆS5)and recommendation (S6,ˆS6), which involve free-text descriptions, we measure phrase-level similarity against clinically meaningful expressions. To evalu- ate alignment, we first use optimal matching–based metrics with similarity scores such as BLEU-4 (Pa- pineni et al., 2002) and ROUGE-L (Lin, 2004): Score(i) j=1 |S(i) j|X e∈S(i) jmax ˆe∈ˆS(i) jSimilarity (e,ˆe), 2Top five conditions in MIMIC-CXR-JPG are Pneumotho- rax, Pneumonia, Edema, Pleural Effusion, and Consolidation.where S(i) j={ek}n k=1andˆS(i) j={ˆek}n′ k=1. Ad- ditionally, to better approximate clinical judgment from an expert’s perspective, we prompt o1-mini (Prompt 8) to directly compare each attribute pair and return a similarity score in the range [0,1]. 3CLEAR-Bench: Attribute-Level Expert Alignment Dataset In this section, we introduce CLEAR-Bench, an expert-curated, attribute-level dataset in collabora- tion with five radiologists. Inspired by recent expert evaluation datasets for chest X-ray reports (Tian et al., 2023; Yu et al., 2023a; Rao et al., 2025), CLEAR-Bench is specifically designed to assess how well automated evaluators like CLEAR align with radiologist judgments. It consists of two anno- tation subsets: expert ensemble labels and expert- curated attributes. We defer full details of the in- struction criteria, interface design, and annotation workflow to Appendix B. Expert ensemble labels. These provide the ground-truth labels for the Presence | https://arxiv.org/abs/2505.16325v1 |
attribute. We randomly selected 100 studies from the validation and test sets of MIMIC-CXR-JPG (Johnson et al., 2024), excluding any training samples and normal studies. Each report was independently annotated from scratch by three board-certified radiologists. During annotation, the radiologists categorized each of 13 CheXpert conditions (Irvin et al., 2019) into one of five categories: confidently absent , likely absent ,neutral ,likely present , and confidently present , based on their best inter- pretation of the report. After the initial round of an- notations, we merged confidently present and likely present into a single category positive , while likely absent andconfidently absent intonegative . We then assessed agreement across annotators. Remaining disagreements were first resolved by majority vote, followed by a consen- sus discussion for any unresolved conflicts. The finalized dataset serves as the ground truth for eval- uating model performance in the Label Extraction Module. Expert-curated attributes. These cover the re- maining five report attributes: first occurrence , change ,severity ,descriptive location , and recommendation . We began by preparing two sets of model-generated attributes, one from Llama-3.1- 8B-Instruct and the other from GPT-4o, for each positive condition identified in the expert ensem- 5 Experiments Pos F1@13 Pos F1@5 Pos F1 (micro) Neg F1@13 Neg F1@5 Neg F1 (micro) LARGE MODELS GPT-4o (base) 0.805 0.929 0.934 0.476 0.648 0.815 GPT-4o (5-shot) 0.795 0.940 0.934 0.510 0.723 0.842 MEDIUM MODELS Llama-3.1-70B-Instruct (base) 0.782 0.890 0.924 0.630 0.850 0.920 Llama-3.1-70B-Instruct (5-shot) 0.794 0.916 0.924 0.744 0.890 0.958 Llama-3.3-70B-Instruct (base) 0.780 0.894 0.925 0.602 0.876 0.926 Llama-3.3-70B-Instruct (5-shot) 0.781 0.907 0.926 0.695 0.892 0.953 SMALL MODELS Llama-3.1-8B-Instruct (base) 0.736 0.880 0.910 0.418 0.660 0.714 Llama-3.1-8B-Instruct (550 finetune) 0.729 0.806 0.905 0.482 0.803 0.949 Qwen2.5-7B-Instruct (base) 0.694 0.834 0.880 0.413 0.616 0.736 Qwen2.5-7B-Instruct (550 finetune) 0.727 0.800 0.905 0.511 0.849 0.953 BASELINES CheXbert (Smit et al., 2020) 0.695 0.833 0.897 0.498 0.877 0.952 CheXpert (Irvin et al., 2019) 0.674 0.811 0.888 0.522 0.831 0.948 ∆Improvement over SOTA +15.8% +12.8% +4.1% +42.5% +1.7% +0.06% Table 2: Evaluation of the label extraction module. CLEAR outperforms existing labelers across all metrics in identifying both positive and negative conditions. Specifically, larger models perform better at capturing positive conditions, while techniques such as 5-shot prompting and supervised fine-tuning significantly improve the detection of negative conditions. ble labels. These two sets were merged and then randomly split into two review sets, each with 50 samples from Llama and 50 from GPT-4o. Each set was independently reviewed by separate radiol- ogists. During curation, each radiologist first rated each attribute as incorrect ,partially correct , orcorrect . For non- correct attributes, the radi- ologist also provided a revised version, which was used to construct the ground-truth attribute set. 4 Experiments Experimental setup. To evaluate the effective- ness and clinical reliability of our proposed CLEAR framework, we conduct experiments using CLEAR-Bench. For the Label Extraction Mod- ule, we compare CLEAR’s performance against two established baselines: the BERT-based labeler CheXbert (Smit et al., 2020) and the rule-based la- beler CheXpert (Irvin et al., 2019), using the Expert Ensemble Labels from CLEAR-Bench. We report F1 scores | https://arxiv.org/abs/2505.16325v1 |
as introduced in Section 2.3. For the De- scription Extraction Module, we evaluate CLEAR using the Expert-Curated Attributes from CLEAR- Bench. As no prior baselines exist for this task, we report expert evaluation scores directly, along with automated metrics defined in Section 2.3. LLM-based labeler achieves substantial gains over existing labelers. We begin with evaluating the performance of the Label Extraction Module. As shown in Table 2, our text generation-based approach (Prompt 1) significantly outperforms the best BERT-based labeler (Smit et al., 2020) and the top rule-based labeler (Irvin et al., 2019) acrossall accuracy metrics. In identifying positive con- ditions, our module achieves a notable improve- ment in accuracy averaged over all 13 medical conditions (+15.8%), with smaller increase on the Top 5 conditions (+12.8%) and the full label pool (+4.1%). This is likely because text generation models can understand the full sentence and over- all report, instead of relying on token-level clas- sification or hard-coded rules. Furthermore, this contextual understanding generalizes across con- ditions, especially for rare conditions (e.g., frac- ture) where BERT-based models struggle due to data imbalance, and unseen patterns (e.g., pleu- ral other) where rule-based systems fail to capture beyond their predefined scope. This advantage is even more evident in negative conditions, which require interpreting implicit cues (e.g., “lungs are clear”). Our module achieves a substantial boost (+42.5%) in average accuracy across all conditions, highlighting once again its strength in semantic understanding beyond explicit mentions. Ablation study of model scales and adaptation. For identifying positive clinical findings, model scale plays a major role, with GPT-4o achieving the highest performance across all accuracy metrics. In contrast, model adaptation strategies, includ- ing both few-shot prompting and supervised fine- tuning, have relatively limited impact compared to each base model. This is likely because the base models already encode sufficient clinical knowl- edge to accurately identify positive findings, and larger model scales are more strongly related with the richness of this knowledge. However, when 6 First Occurrence Change Severity Descriptive Location Recommendation Metric GPT-4o Llama 8B GPT-4o Llama 8B GPT-4o Llama 8B GPT-4o Llama 8B GPT-4o Llama 8B EXPERT EVALUATION SCORES Experts (condition averaged) 0.818 0.685 0.837 0.685 0.809 0.565 0.857 0.761 0.933 0.474 Experts (report averaged) 0.783 0.680 0.867 0.688 0.771 0.583 0.872 0.763 0.940 0.416 Experts (micro) 0.777 0.662 0.855 0.663 0.777 0.570 0.867 0.757 0.936 0.404 ACCURACY METRICS Acc. (condition averaged) 0.740 0.688 0.710 0.589 0.682 0.470 – – – – Acc. (report averaged) 0.755 0.679 0.759 0.596 0.685 0.532 – – – – Acc. (micro) 0.737 0.665 0.754 0.575 0.671 0.494 – – – – SIMILARITY METRICS o1-mini (micro) – – – – – – 0.785 0.739 0.888 0.361 ROUGE-L (micro) – – – – – – 0.686 0.672 0.887 0.268 BLEU-4 (micro) – – – – – – 0.500 0.402 0.885 0.263 Average (experts) 0.793 0.676 0.853 0.679 0.786 0.573 0.865 0.760 0.936 0.431 Average (all) 0.768 0.677 0.797 0.633 0.733 0.536 0.761 0.682 0.911 0.364 ∆(GPT-4o −Llama) +0.091 +0.164 +0.197 +0.079 +0.547 ∗A dash (–) indicates the metric is not applicable for this attribute. ∗Bold values | https://arxiv.org/abs/2505.16325v1 |
highlight the highest scores per metric. Colored cells distinguish GPT-4o (green) from Llama 8B (yellow). ∗The bottom row shows the difference between GPT-4o and Llama 8B for the "Average (all)" metric. Table 3: Evaluation of the description extraction module. Expert ratings are averaged across all samples (0 = incorrect, 0.5 = partially correct, 1 = correct). According to radiologists’ clinical judgment, CLEAR can accurately extract attribute-level information from free-text reports. Additionally, GPT-4o is consistently preferred over Llama- 3.1-8B-Instruct, though Llama performs reasonably well, especially on descriptive location , and remains a low-cost, open-source option. it comes to negative mentions, model adaptation strategies stand out, with all metrics improving no- tably across scales. The reason is that these strate- gies effectively incorporate expert-derived “side” information, which is typically not captured by base models during pre-training, through few-shot examples or supervised training data. Specifically, among different strategies, supervised fine-tuning consistently outperforms few-shot prompting, with average gains of 26.8% for small models from fine- uning, 7.9% for medium models from few-shot, and 7.3% for large models from few-shot. LLMs, especially GPT-4o, excel at fine-grained attribute extraction. We next probe our descrip- tion extraction module to assess how reliably a unified language model can handle all five fine- grained attributes (see Table 3). Overall, GPT- 4o shows strong performance across all five at- tributes, achieving the highest average score of 0.911 ( recommendation average all) and a mini- mum of 0.733 ( severity ). When analyzing by task type, GPT-4o performs better on IE tasks (location andrecommendation ), with an aver- age score of 0.836, particularly for attributes that involve highly formulaic language (e.g., “follow- up imaging recommended to assess the resolu- tion of opacity” for recommendation ). In con- trast, it achieves a relatively lower score of 0.766 on QA tasks ( first occurrence ,change , andAutomated Metric Corr. with Expert Scoring Accuracy Metrics produced by CLEAR Acc. (condition averaged) 0.894 Acc. (report averaged) 0.908 Acc. (micro) 0.915 Similarity Metrics produced by CLEAR o1-mini (micro) 0.994 ROUGE-L (micro) 0.977 BLEU-4 (micro) 0.811 Table 4: Pearson correlation between CLEAR and ex- pert scores. All of automated metrics generated by CLEAR show strong alignment with expert evaluations. severity ), which typically require deeper clinical contextual understanding. In comparison, Llama- 3.1-8B-Instruct (a small-scale model) shows mixed performance across attributes. In QA tasks, it cap- tures temporal information reasonably well, scor- ing 0.677 for first Occurrence average all and 0.633 for change , though its interpretation of clini- cal findings is weaker (0.536 for severity ). As for IE tasks, hallucinations significantly affect perfor- mance. But with a customized terminology list (see Table 7), it achieves 0.682 on location , the closest to GPT-4o. However, unrelated descriptive phrases (e.g., “signs of generalized fluid overload”) signifi- cantly lower recommendation score to 0.364. CLEAR aligns well with expert ratings. Gener- ally, all the implementations of CLEAR are highly correlated with expert scoring, as shown in Ta- 7 ble 4. However, automated metrics are typically slightly lower than expert scores, as observed in Table 3. This is because similarity metrics based on | https://arxiv.org/abs/2505.16325v1 |
ROUGE-L and BLEU-4 prioritize exact matches against ground truth, whereas expert scoring in- cludes a Partially Correct category, allowing some tolerance for clinically reasonable but not perfectly matched responses. This distinction is further supported by the exceptionally high correla- tion of o1-mini scores with expert ratings, reaching 0.994. Compared to other lexical metrics, o1-mini can more effectively capture semantic and clini- cal alignment, making it a closer proxy to expert judgment. 5 Related Work Lexical metrics. Traditional word-overlap met- rics such as BLEU (Papineni et al., 2002), ROUGE (Lin, 2004),and METEOR (Banerjee and Lavie, 2005) are commonly used in natural lan- guage generation tasks and are therefore also com- monly applied to radiology report generation. How- ever, these metrics fail to capture subtle semantic nuances, such as negations or synonyms, which are critical in the clinical domain. Embedding- based metrics like BERTScore (Zhang et al., 2020) improve on semantic matching but remain inade- quate in capturing nuanced semantics and domain- specific medical terms, thereby missing clinically important errors. Clinical efficacy metrics. To bridge the gap be- tween surface-level fluency and clinical correct- ness, domain-specific metrics have been introduced. Label-based metrics such as CheXpert (Irvin et al., 2019) map reports to 14 predefined clinical la- bels and measure classification accuracy, but their rule-based pipelines propagate annotation noise. CheXbert (Smit et al., 2020) improves seman- tic understanding over CheXpert by fine-tuning BERT-based classifiers; however, it still lags be- hind recent LLMs due to the limited capacity of BERT compared to newer and more powerful lan- guage models. More recent entity-centric meth- ods such as RadGraph F1 (Jain et al., 2021), Rad- Graph2 (Khanna et al., 2023), MEDCON (Yim et al., 2023) and RaTEScore (Zhao et al., 2024) capture subject–relation–object triples. Although these approaches effectively identify and compare medical entities and their relationships, they often lack the granularity to evaluate specific attributes such as severity, temporal progression, or treat-ments. To better align automatic metrics with ra- diologist judgments, RadCliQ (Yu et al., 2023b) combines BLEU, BERTScore, CheXbert similarity, and RadGraph F1 into a weighted score learned from 160 radiologist-annotated report pairs (ReX- Val). These annotations are provided at an aggre- gate level, quantifying the total number of clinically significant and insignificant errors without distin- guishing specific clinical attributes. LLM-based metrics. More recently, researchers have been using LLMs to assess radiology reports. Several methods, including GREEN and CheX- prompt, build on six categories of the clinical-error taxonomy introduced in RadCliQ. GREEN (Ost- meier et al., 2024) tallies the number of errors and matched findings of each type and then aggregates them into a single report-level score, which limits granularity and makes it difficult to isolate specific mistakes. CheXprompt (Zambrano Chaves et al., 2025) uses GPT-4 to quantify clinically significant and insignificant errors in radiology reports, catego- rizing them into six predefined types. Similarly, it focuses primarily on counting these errors without delving into the nuanced contextual attributes of each error instance. FineRadScore (Huang et al., 2024) takes a different route: it calculates the min- imum line-by-line edits required to transform a generated report into a reference | https://arxiv.org/abs/2505.16325v1 |
report. While this encourages precision, it penalizes semantically equivalent but differently phrased outputs. Rad- Fact (Bannur et al., 2024) decomposes each report into atomic sentences and uses LLM to determine whether each generated sentence is entailed by the reference report, which does not differentiate dif- ferent types of clinical errors or severity. 6 Conclusion We present CLEAR, the first clinically grounded, attribute-level evaluation framework that leverages LLMs to convert free-text radiology reports into a structured tabular format. CLEAR consists of three components: (1) a label extraction module to assess the accurate identification of medical condi- tions; (2) a description extraction module to evalu- ate the precision of condition descriptions; and (3) a scoring module to compile multi-metric evalua- tion results. We also introduce CLEAR-Bench, an expert-curated alignment dataset covering 6 report attributes and 13 medical conditions. Our experi- ments show that CLEAR can effectively identify clinical conditions, faithfully extract attribute-level 8 information in line with clinical validation, and provide automated metrics that serve as reliable proxies for expert scoring. Limitations While CLEAR provides a clinically grounded framework and demonstrates strong alignment with expert clinical assessment, it has several limitations. First, like all existing evaluation metrics, CLEAR relies solely on ground-truth reports without in- corporating image information, overlooking the fact that reference reports may not fully capture all relevant findings present in the image. Future work could explore integrating image-based evalua- tion to better reflect clinical completeness. Second, CLEAR is built on the CheXpert label structure, which is limited in both granularity and anatomi- cal coverage. Extending the framework to include additional specialties such as breast imaging, car- diology, and gastroenterology in the future could enhance its generalizability. Lastly, although we prioritize high-quality annotations, both the train- ing and evaluation datasets remain relatively small due to the common tradeoff between annotation quality and dataset scale. References Anthropic. 2025. Claude 3.7 sonnet system card. Ac- cessed: 2025-05-19. Satanjeev Banerjee and Alon Lavie. 2005. Meteor: An automatic metric for mt evaluation with improved cor- relation with human judgments. In Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summariza- tion, pages 65–72. Shruthi Bannur, Kenza Bouzid, Daniel C. Castro, An- ton Schwaighofer, Sam Bond-Taylor, Maximilian Ilse, Fernando P’erez-Garc’ia, Valentina Salvatelli, Harshita Sharma, Felix Meissen, Mercy Prasanna Ranjit, Shaury Srivastav, Julia Gong, Fabian Falck, Ozan Oktay, Anja Thieme, Matthew P. Lungren, Maria T. A. Wetscherek, Javier Alvarez-Valle, and Stephanie L. Hyland. 2024. Maira-2: Grounded radi- ology report generation. arXiv , abs/2406.04449. Suhana Bedi, Yutong Liu, Lucy Orr-Ewing, Dev Dash, Sanmi Koyejo, Alison Callahan, Jason A. Fries, Michael Wornow, Akshay Swaminathan, Lisa So- leymani Lehmann, Hyo Jung Hong, Mehr Kashyap, Akash R. Chaurasia, Nirav R. Shah, Karandeep Singh, Troy Tazbaz, Arnold Milstein, Michael A. Pfeffer, and Nigam H. Shah. 2025. Testing and eval- uation of health care applications of large language models: A systematic review. JAMA , 333(4):319– 328.Deep Ganguli, Nicholas Schiefer, Marina Favaro, and Jack Clark. 2023. Challenges in evaluating AI sys- tems. Alyssa Huang, Oishi Banerjee, Kay Wu, Ed- uardo Pontes Reis, and Pranav Rajpurkar. 2024. Fin- eradscore: A | https://arxiv.org/abs/2505.16325v1 |
radiology report line-by-line evalua- tion technique generating corrections with severity scores. In Machine Learning for Healthcare Confer- ence. PMLR. Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Mark- lund, Behzad Haghgoo, Robyn Ball, Katie Shpan- skaya, and 1 others. 2019. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In Proceedings of the AAAI conference on artificial intelligence , volume 33, pages 590–597. Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven Truong, Du Nguyen Duong Nguyen Duong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew Lungren, Andrew Ng, Curtis Langlotz, Pranav Rajpurkar, and Pranav Rajpurkar. 2021. Radgraph: Extracting clini- cal entities and relations from radiology reports. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks , vol- ume 1. Alistair Johnson, Matthew Lungren, Yifan Peng, Zhiy- ong Lu, Roger Mark, Seth Berkowitz, and Steven Horng. 2024. Mimic-cxr-jpg - chest radiographs with structured labels (version 2.1.0). https://doi. org/10.13026/jsn5-t979 . Alistair EW Johnson, Tom J Pollard, Nathaniel R Green- baum, Matthew P Lungren, Chih-ying Deng, Yifan Peng, Zhiyong Lu, Roger G Mark, Seth J Berkowitz, and Steven Horng. 2019. Mimic-cxr-jpg, a large pub- licly available database of labeled chest radiographs. arXiv preprint arXiv:1901.07042 . Sameer Khanna, Adam Dejl, Kibo Yoon, Steven QH Truong, Hanh Duong, Agustina Saenz, and Pranav Rajpurkar. 2023. Radgraph2: Modeling disease pro- gression in radiology reports via hierarchical informa- tion extraction. In Machine Learning for Healthcare Conference , pages 381–402. PMLR. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023. Effi- cient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles . Curtis P. Langlotz. 2015. The Radiology Report: A Guide to Thoughtful Communication for Radiolo- gists and Other Medical Professionals . CreateSpace Independent Publishing Platform, North Charleston, SC. Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out , pages 74–81. 9 Nestor Maslej, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kariuki, Emily Capstick, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Rus- sell Wald, Tobi Walsh, Armin Hamrah, Lapo Santar- lasci, and 4 others. 2025. The ai index 2025 annual report. Technical report, AI Index Steering Com- mittee, Institute for Human-Centered AI, Stanford University, Stanford, CA. https://hai.stanford. edu/ai-index/2025-ai-index-report . Daniel McDuff, Mike Schaekermann, Tao Tu, Anil Palepu, Amy Wang, Jake Garrison, Karan Singhal, Yash Sharma, Shekoofeh Azizi, Kavita Kulkarni, and 1 others. 2025. Towards accurate differential diag- nosis with large language models. Nature , pages 1–7. OpenAI. 2025. Introducing gpt-4.5. Accessed: 2025- 05-19. Sophie Ostmeier, Justin Xu, Zhihong Chen, Maya Varma, Louis Blankemeier, Christian Bluethgen, Arne Md, Michael Moseley, Curtis Langlotz, Akshay Chaudhari, and 1 others. 2024. Green: Generative radiology report evaluation and error notation. In Findings of the Association for Computational Lin- guistics: EMNLP 2024 , pages 374–390. Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. Bleu: a method for automatic evalu- ation | https://arxiv.org/abs/2505.16325v1 |
of machine translation. In Proceedings of the 40th annual meeting of the Association for Computa- tional Linguistics , pages 311–318. Magdalini Paschali, Zhihong Chen, Louis Blankemeier, Maya Varma, Alaa Youssef, Christian Bluethgen, Curtis Langlotz, Sergios Gatidis, and Akshay Chaud- hari. 2025. Foundation models in radiology: What, how, why, and why not. Radiology , 314(2):e240597. V . Rao, S. Zhang, J. Acosta, S. Adithan, and P. Ra- jpurkar. 2025. Rexerr-v1: Clinically meaningful chest x-ray report errors derived from mimic-cxr. PhysioNet. Maribeth Rauh, Nahema Marchal, Arianna Manzini, Lisa Anne Hendricks, Ramona Comanescu, Canfer Akbulut, Tom Stepleton, Juan Mateos-Garcia, Stevie Bergman, Jackie Kay, Conor Griffin, Ben Bariach, Ia- son Gabriel, Verena Rieser, William Isaac, and Laura Weidinger. 2024. Gaps in the safety evaluation of generative ai. Proceedings of the AAAI/ACM Confer- ence on AI, Ethics, and Society , 7(1):1200–1217. Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pa- reek, Andrew Y Ng, and Matthew Lungren. 2020. Combining automatic labelers and expert annotations for accurate radiology report labeling using bert. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages 1500–1519. Kevin Tian, S. J. Hartung, A. A. Li, J. Jeong, F. Behzadi, J. Calle-Toro, S. Adithan, M. Pohlen, D. Osayande,and P. Rajpurkar. 2023. Refisco: Report fix and score dataset for radiology report generation. PhysioNet. Maxim Tkachenko, Mikhail Malyuk, Andrey Holmanyuk, and Nikolai Liubimov. 2020- 2022. Label Studio: Data labeling soft- ware. Open source software available from https://github.com/heartexlabs/label-studio. Tao Tu, Mike Schaekermann, Anil Palepu, Khaled Saab, Jan Freyberg, Ryutaro Tanno, Amy Wang, Brenna Li, Mohamed Amin, Yong Cheng, Elahe Vedadi, Nenad Tomasev, Shekoofeh Azizi, Karan Singhal, Le Hou, Albert Webson, Kavita Kulkarni, S. Sara Mahdavi, Christopher Semturs, and 7 others. 2025. Towards conversational diagnostic artificial intelligence. Na- ture, pages 1–9. Wen-wai Yim, Yujuan Fu, Asma Ben Abacha, Neal Snider, Thomas Lin, and Meliha Yetisgen. 2023. Aci- bench: a novel ambient clinical intelligence dataset for benchmarking automatic visit note generation. Scientific data , 10(1):586. F. Yu, M. Endo, R. Krishnan, I. Pan, A. Tsai, E. P. Reis, E. Kaiser Ururahy Nunes Fonseca, H. Lee, Z. Shak- eri, A. Ng, C. Langlotz, V . K. Venugopal, and P. Ra- jpurkar. 2023a. Radiology report expert evaluation (rexval) dataset (version 1.0.0). Feiyang Yu, Mark Endo, Rayan Krishnan, Ian Pan, Andy Tsai, Eduardo Pontes Reis, Eduardo Kaiser Ururahy Nunes Fonseca, Henrique Min Ho Lee, Zahra Shakeri Hossein Abad, Andrew Y Ng, and 1 others. 2023b. Evaluating progress in automatic chest x-ray radiology report generation. Patterns , 4(9). Juan Manuel Zambrano Chaves, Shih-Cheng Huang, Yanbo Xu, Hanwen Xu, Naoto Usuyama, Sheng Zhang, Fei Wang, Yujia Xie, Mahmoud Khademi, Ziyi Yang, Hany Awadalla, Julia Gong, Houdong Hu, Jianwei Yang, Chunyuan Li, Jianfeng Gao, Yu Gu, Cliff Wong, Mu Wei, and 8 others. 2025. A clinically accessible small multimodal radiology model and evaluation metric for chest x-ray findings. Nature Communications , 16(1):3108. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evalu- ating text generation with BERT. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenRe- | https://arxiv.org/abs/2505.16325v1 |
view.net. Weike Zhao, Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. 2024. RaTEScore: A metric for radiology report generation. In Proceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages 15004–15019, Miami, Florida, USA. Association for Computational Linguistics. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, and 1 others. 10 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Pro- cessing Systems , 36:46595–46623. Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. 2024. Llamafactory: Unified efficient fine-tuning of 100+ language models. In Proceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (Volume 3: System Demonstra- tions) , Bangkok, Thailand. Association for Computa- tional Linguistics. Appendix A Open-sourced Artifacts We will formally release code package for CLEAR on GitHub at the camera-ready stage. The current version supports both open-source models via the vLLM backend and closed-source models through the Azure OpenAI API. Our collected ground-truth dataset, CLEAR- Bench, and related data documentation will also be made publicly available on Physionet to support future research in this area. B Data Annotation and Curation We accessed MIMIC-CXR-JPG data by following the required steps on https://physionet.org/ content/mimic-cxr-jpg/2.1.0/ . We first reg- istered and applied to be a credentialed user, and then completed the required training of CITI Data or Specimens Only Research. Data license can be found at https://physionet.org/content/ mimic-cxr-jpg/view-license/2.1.0/ . During each human annotation process, we fol- low a traditional paradigm: initial pilot rounds are conducted to gather user feedback, followed by formal, independent large-scale annotation, data analysis for quality control and final resolution via consensus discussion. Our annotation platform is built upon an open source data labeling tool, Label Studio (Tkachenko et al., 2020-2022). B.1 Label Structure Refinement During the interaction of pilot training, we closely work with all involved radiologists and collect a lot of valuable feedback for user experience with designed interfaces and task instruction. After summarizing input feedback, we recog- nize some shared and repeatedly mentioned issues in the 4-type label structure of MIMIC-CXR-JPG (see Figure 3): (1) The “unmentioned” category has a high degree of overlap with other categories,MIMIC-CXR-JPG Labeling Criteria Positive (1.0) : The label is positively mentioned in the report and present in one or more associated images. Example: “A large pleural effusion” Negative (0.0) : The label is negatively mentioned in the report and should not be present in any associated image. Example: “No pneumothorax.” Uncertain (-1.0) : The label is either: (1) mentioned with uncertainty, so presence in the image is unclear; or (2) described ambiguously, with uncertain exis- tence. Explicit uncertainty: “The cardiac size cannot be eval- uated.” Ambiguous language: “The cardiac contours are sta- ble.” Unmentioned (Missing) : The label is not mentioned in the report at all. Figure 3: 4-type labeling criteria in MIMIC. particularly with “negative” labels. This is because radiologists often do not explicitly state negative findings in the report. However, indirect phrases such as “Lungs are | https://arxiv.org/abs/2505.16325v1 |
clear” can implicitly negate a wide range of lung-related abnormalities. (2) Additionally, different radiologists have varying tendencies in labeling conditions. More conserva- tive radiologists may lean toward assigning “un- certain” rather than “positive” labels, even when the evidence suggests a likely presence. This in- consistency introduces label noise and ambiguity, particularly when these labels are used for super- vised training or evaluation purposes. Therefore, we refined the original MIMIC la- bel structure into a “5+1” annotation framework. The “5” refers to an extension of MIMIC’s original “Positive,” “Negative,” and “Uncertain” categories into five more nuanced types, as shown in Figure 4. The “+1” refers to retaining the “Unmentioned” la- bel as a separate flag. Specifically, radiologists are asked to select one of the five labels for each con- dition and additionally indicate whether this label is explicitly mentioned in the report or not. After collecting radiologist responses, we map the five types into a final three-type scheme for downstream use: “Confidently Present” and “Likely Present” are merged into “Positive,” “Con- fidently Absent” and “Likely Absent” into “Nega- tive,” and “Neutral” is renamed as “Unclear.” We then proceed with inter-rater alignment checks for quality control. Notably, the “mentioned” flag is not incorporated into the final label itself but serves as a supporting indicator for data managers to dif- ferentiate between labeling disagreements due to 11 Our Refined Labeling Criteria Confidently Absent : The condition is clearly stated as not present in the report. Example: “No pneumothorax.” Likely Absent : The report implies the condition is likely absent, but the language is ambiguous or uncer- tain. Example: “Heart size is normal though increased.” Neutral : The report does not clearly indicate pres- ence or absence. Explicit uncertainty: “The cardiac size cannot be eval- uated.” Ambiguous language: “The cardiac contours are sta- ble.” Likely Present : The report suggests the condition may be present, but uses uncertain or ambiguous lan- guage. Example: “Likely reflecting compressive atelectasis.” Confidently Present : The condition is clearly stated as present in the report. Example: “A small right pleural effusion.” Figure 4: Our refined 5-type labeling criteria during expert annotation. Figure 5: Interface for Label Annotation. quality issues versus differences in individual clini- cal interpretation. This overall process enables us to accommodate variability in radiologist judgment while maintaining high annotation quality. B.2 Expert-in-the-loop Dataset Curation We first exclude 2 cases without any “FINDINGS” or “IMPRESSION” and 30 cases labeled as “No Finding” in the radiologist annotation dataset from MIMIC-CXR-JPG (containing 687 studies in total). Then, we randomly select 20 cases to serve as a pilot set for initial review and refinement of the process. We then prompt GPT-4o to generate condition labels following the same guidelines used in the original MIMIC documentation for remaining stud- ies excluded 20 pilot cases. After identifying dis- crepancies between the model-generated labels and the original dataset annotations, we isolate the sus- Figure 6: Interface for Attribute Curation. pected noisy labels for further review. For each case, we extract only the relevant report sections (FINDINGS and IMPRESSION), with no images involved, and present them to | https://arxiv.org/abs/2505.16325v1 |
a board- certified radiologist. The radiologist independently re-annotates the report from scratch based on their clinical judgment. During the curation, we discard 5 cases due to GPT-4o generation failures. To manage the annota- tion workload, we limit each review to reports with one to five mismatched conditions per case. The full curation process took approximately one month, resulting in 550 finalized reports, each annotated with 13 condition labels. Task instruction can be checked in Figure 7 and interface can be checked in Figure 5. B.3 CLEAR-Bench: Expert Ensemble After excluding "No Finding" cases and those al- ready annotated in the curation stage, we selected 5 cases for pilot training and randomly sampled 100 reports from the test and validation sets of MIMIC- CXR-JPG to construct our final evaluation dataset. Following a brief onboarding process using 5 pilot cases, we collected independent annotations from three radiologists, each labeling the 100 re- ports from scratch. After an initial round of major- ity voting, 25 reports with 32 individual condition labels in total remained unresolved. These were finalized through a single round of discussion and consensus among the experts. The full expert ensemble workflow was com- pleted over the course of three months, resulting in 100 fully annotated reports, each with 13 condition labels. Task instruction can be checked in Figure 7 and interface can be checked in Figure 5. B.4 CLEAR-Bench: Attribute Curation The blueprint for attribute design was initially in- spired by the concept of an “Attribute-Value For- 12 mat” proposed by Dr. Langlotz in his practical guide to writing radiology reports (Langlotz, 2015, 207). Driven by this concept, we generated a list of commonly used report attributes with the assis- tance of GPT-4o, and refined it through discussion with our collaborating research radiologist, who is also a co-author. Together, we determined which attributes to include, revise, or remove. During this process, we not only developed a concise yet comprehensive attribute structure but also collected useful example phrases and sentences for each at- tribute. These examples were later incorporated into the prompts used in the Description Extraction Module (see Appendix D). The final version of the prompt set and word list was also reviewed and approved by a clinical radiologist. We curated attributes using the same 100 studies described earlier, excluding 2 cases that lacked any positively identified conditions in expert ensemble labels. Following a round of pilot training, the formal curation process proceeded as detailed in Section 3. After collecting radiologist responses, we conducted a second round of quality control to finalize the ground-truth attributes. The full human curation process took approximately one month. Task instructions are shown in Figure 8, and the annotation interface is illustrated in Figure 6. C CLEAR: Implementation Details Base Model GAS LR Epochs Llama3.1-8B-Instruct 1 7.0×10−64 Qwen2.5-7B-Instruct 1 9.0×10−65 Table 5: Hyperparameter search results. GAS denotes the number of gradient-accumulation steps, LR the learning rate, and Epochs the total training epochs. Supervised finetuning details. All fine-tuned models were obtained through supervised fine- tuning with LLaMA-Factory (Zheng et al., 2024). To identify an optimal configuration, we developed | https://arxiv.org/abs/2505.16325v1 |
an automated hyperparameter optimization (HPO) framework that combines five-fold cross-validation with a grid search. Learning rate, number of epoch, and gradient accumulation steps are three objects to be optimized. For learning rate, searching space is[3.0e−6,3.0e−5], with an interval of 2.0e−6. For epoch, searching space is {2,3,4,5}. For gradient accumulation steps, searching target is {1,2,4}. We conduct extensive experiments to assess hy- perparameters’ influence. A total of 360 modelsare finetuned for one base model to determine the best hyperparameter setting. The best-performing settings, summarized in Table 5, are used for all experiments reported in Table 2. Hyperparameter optimization and model training are performed on NVIDIA A100 80G and NVIDIA H100 94G GPUs. The HPO stage takes 93 h 51 m 20 s on four A100s and 14 h 39 m 36 s on four H100s. Inference details for local models. We serve the models locally with vLLM (0.8.5.post1) (Kwon et al., 2023). Inference runs with a temperature of 1e-5 and a max_tokens of 4,096; all other sampling parameters remain at their default settings. A single NVDIA A100 80G is sufficient for inference under this setting. Model Standard Pricing (per 1M Tokens) GPT-4o-2024-1120 (Global) Input: $2.50 Cached: $1.25 Output: $10.00 o1-mini-2024-09-12 (Global) Input: $1.10 Cached: $0.55 Output: $4.40 Table 6: Standard API pricing per 1M tokens for GPT- 4o and o1-mini models, based on Azure OpenAI pricing: https://azure.microsoft.com/en-us/pricing/ details/cognitive-services/openai-service/ #pricing . API Details We access OpenAI’s GPT-4o (2024- 11-20) and o1-mini (2024-09-12) via Microsoft’s Azure. Pricing details can be checked in Table 6. D Template & Terminology List 13 Thank you very much for your support in our human annotation process! To begin with, please register at https: //physionet.org/content/mimic-cxr-jpg/2.1.0/ and sign the data agreement before the study. Feel free to reach us at { EMAIL } if you encounter any issue or any questions during the process. Overview: Task Description In this task, you will be extracting clinical information from { NUM} radiology reports in total. You will not be shown the corresponding images, so you are being asked to interpret each report, as written, for the extent to which the presence of {NUM} conditions is captured. It is important to note that some reports may have empty FINDINGS or IMPRESSION sections due to limitations in the original MIMIC-CXR-JPG database. Please follow the labeling instructions as below. INSTRUCTIONS: For each case, you will be presented with a single radiology report. Your objective is to choose the single most appropriate criterion among 5 options (see below) for each of the { NUM} conditions AND note whether each condition is explicitly mentioned in the report. Please base your decisions solely on the provided report. CRITERIA: {See Figure 4 } Interface User Guide {Account Information and Usage Tips } Figure 7: Instruction Template for Label Annotation Task Thank you very much for your support in our human annotation process! To begin with, please register at https: //physionet.org/content/mimic-cxr-jpg/2.1.0/ and sign the data agreement before the study. Feel free to reach us at { EMAIL } if you encounter any issue or any questions during the process. Overview: Task | https://arxiv.org/abs/2505.16325v1 |
Description This curation task is to identify fine-grained features—such as location, severity, and treatment—related to specific medical conditions (e.g., edema, atelectasis, support devices) in radiology reports. You will review { NUM} text-only reports (no X-ray images) and assess the accuracy of feature annotations generated by an AI model. Each report includes 13 predefined medical conditions, but you will only see those that were positively labeled by human annotators. As a result, the number of conditions shown per report may vary. For each positive condition, the AI extracts fine-grained details (e.g., location, severity), which you need to review. Start by marking the model’s answer as correct, partially correct, or incorrect. If it’s incorrect, enter the corrected version in the provided text box. [optional] If you’d like to understand how the AI generated its responses, you can review the prompts we used at { See Appendix D }. Interface User Guide {Account Information and Usage Tips } Figure 8: Instruction Template for Attribute Curation Task 14 Prompt 1: Presence System Instruction: You are a radiologist reviewing a piece of radiology report to assess the presence of 13 specific medical conditions. Conditions to evaluate: Cardiomegaly, Enlarged Cardiomediastinum, Atelectasis, Consolidation, Edema, Lung Lesion, Lung Opacity, Pneumonia, Pleural Effusion, Pneumothorax, Pleural Other, Fracture, Support Devices. Each medical condition in the radiology report must be categorized using one of the following labels: "positive", "negative" or "unclear". The criteria for each label are: •"positive": The condition is indicated as present in the report. •"negative": The condition is indicated as not present in the report. •"unclear": The report does not indicate a clear presence or absence of the condition. The user will provide you with a piece of radiology report as input. Return your results in the following JSON format: <TASK1>{ "Cardiomegaly": "positive"|"negative"|"unclear", "Enlarged Cardiomediastinum": "positive"|"negative"|"unclear", "Atelectasis": "positive"|"negative"|"unclear", "Consolidation": "positive"|"negative"|"unclear", "Edema": "positive"|"negative"|"unclear", "Lung Lesion": "positive"|"negative"|"unclear", "Lung Opacity": "positive"|"negative"|"unclear", "Pneumonia": "positive"|"negative"|"unclear", "Pleural Effusion": "positive"|"negative"|"unclear", "Pneumothorax": "positive"|"negative"|"unclear", "Pleural Other": "positive"|"negative"|"unclear", "Fracture": "positive"|"negative"|"unclear", "Support Devices": "positive"|"negative"|"unclear" } </TASK1> User Input: FINDINGS: {findings} IMPRESSION: {impression} Prompt 1 Prompt 2: First Occurrence System Instruction: You are a radiologist reviewing a piece of radiology report to extract features for a specific condition, which was already marked as positive during the initial read of this same report. Please determine from the given report (i.e., current study) whether {condition} is being identified for the first time in current study ["current" ], or if the report indicates it was already present or noted in a prior study ["previous" ]. If unmentioned, respond with ["N/A" ]. Only choose one of the following: ["current" ],["previous" ], or ["N/A" ]. Example answer: ["current" ] User Input: FINDINGS: {findings} IMPRESSION: {impression} Prompt 2 15 Prompt 3: Change System Instruction: You are a radiologist reviewing a piece of radiology report to extract features for a specific condition, which was already marked as positive during the initial read of this same report. Please determine from the given report whether {condition} is improving, stable, or worsening according to the given report. If the status is not mentioned, respond with ["N/A" ]. If the report describes multiple | https://arxiv.org/abs/2505.16325v1 |
statuses, respond with ["mixed" ]. Only choose one of the following: ["improving" ],["stable" ],["worsening" ],["mixed" ]or["N/A" ]. Example answer: ["stable" ] User Input: FINDINGS: {findings} IMPRESSION: {impression} Prompt 3 Prompt 4: Severity System Instruction: You are a radiologist reviewing a piece of radiology report to extract features for a specific condition, which was already marked as positive during the initial read of this same report. Please determine from the given report whether {condition} is mild, moderate, or severe according to the given report. If the status is not mentioned, respond with ["N/A" ]. If the report describes multiple statuses, respond with ["mixed" ]. Only choose one of the following: ["mild" ], ["moderate" ],["severe" ],["mixed" ]or["N/A" ]. Example answer: ["mild" ] User Input: FINDINGS: {findings} IMPRESSION: {impression} Prompt 4 Prompt 5: Descriptive Location System Instruction: You are a radiologist reviewing a piece of radiology report to extract features for a specific condition, which was already marked as positive during the initial read of this same report. Please identify the location(s) of {condition} described in the given report. Extract and return a list of phrases that mention the anatomical location(s) {location} specifically related to {condition}. For each location, include any relevant descriptors descriptor and any associated status {status}. {note} If multiple phrases refer to the same location, merge them into one single entry using the most complete, informative, and non-redundant phrasing for that unique area. Format your output as one single list in the following format: ["entry-1" ,"entry-2" ,..., "entry-n" ]. If nothing is mentioned, return ["N/A" ]. Example answer: ["left lower lobe compressive atelectasis" ,"right middle lobe bibasilar atelectasis" ] User Input: FINDINGS: {findings} IMPRESSION: {impression} Prompt 5: Additional Notes: location/descriptor/status/note are a list of example key words or phrases for each condition collected from radiologists, such as (e.g., compressive, segmental, focal, terminal, peripheral, etc.). 16 Condition Location Descriptor Status Note Atelectasis (e.g., left upper, right lower, whole lung, etc.)(e.g., compressive, seg- mental, focal, terminal, pe- ripheral, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.) Cardiomegaly (e.g., mild, moderate, se- vere, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.) Consolidation (e.g., left upper, right lower, whole lung, etc.)(e.g., segmental, focal, ter- minal, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.) Edema (e.g., medial (near hilum), middle, lateral (periph- eral), etc.)(e.g., interstitial, alveolar, minimal, mild, moderate, severe, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.) Enlarged Cardiome- diastinum(e.g., mild, moderate, se- vere, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.) Fracture (e.g., ribs, cervicothoracic vertebra, etc.)(e.g., simple or closed, compound or open, incom- plete or partial, complete, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.) Lung Lesion (e.g., central, peripheral, sub-pleural, entire pleural space, etc.)(e.g., density, internal composition, shape, mar- gin, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.)Explicitly refer to a lung lesion (e.g., nodules, masses, infiltrates, metas- tases, etc.) and ignore findings unre- lated to lung lesions. Lung Opacity (e.g., left upper, right lower, perihilar, etc.)(e.g., interstitial, alveolar, diffuse, focal, dense, ill- defined, faint, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.) Pleural Effusion (e.g., left, right, entire pleural space, etc.)(e.g., subpulmonic, | https://arxiv.org/abs/2505.16325v1 |
pos- terior, loculated, lobular, small, moderate, large, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.) Pneumonia (e.g., left upper, right lower, whole lung, etc.)(e.g., segmental, focal, ter- minal, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.) Pneumothorax (e.g., left upper, right lower, etc.)(e.g., simple, tension, open, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.) Pleural Other (e.g., left upper, right lower, entire pleural space, etc.)(e.g., subpulmonic, poste- rior, loculated, lobular, dif- fuse, focal, etc.)(e.g., improving, worsen- ing, stable, unchanged, new, etc.)Do not include findings that pertain solely to Pleural Effusion; only in- clude findings related to other pleu- ral abnormalities (e.g., thickening, plaques, etc.). Support Devices Exclude any mention of device re- moval. Only include information re- lated to existing or currently present devices. Table 7: Key Words List for Location Prompt (extracted using GPT-4o, then discussed and confirmed by two radiologists) 17 Prompt 6: Recommendation System Instruction: You are a radiologist reviewing a piece of radiology report to extract features for a specific condition, which was already marked as positive during the initial read of this same report. Please identify treatment(s)/follow-up(s) associated with {condition} in the given report. Extract and return a list of phrases that only describe specific treatment(s)/follow-up(s) recommended in relation to condition. Do not include any phrase that merely describes the condition without any treatment/follow-up. Each treatment/follow-up should be a single entry. Format your output as a single list in the following format: ["entry-1" ,"entry-2" ,..., "entry-n" ]. If no action is mentioned, return ["N/A" ]. Example answer: ["follow-up CT scheduled in 3 months" ,"routine annual imaging advised" ] User Input: FINDINGS: {findings} IMPRESSION: {impression} Prompt 6 Prompt 7: Urgency System Instruction: You are a radiologist reviewing a piece of radiology report to extract features for a specific condition, which was already marked as positive during the initial read of this same report. Please determine from the given report whether {condition} requires immediate, short-term, or long-term treatment/follow-up (e.g., Immediate: Urgent chest tube placement recommended; Short-term: Recommend follow-up chest X-ray in 1-2 weeks; Long-term: Routine annual imaging advised). If unmentioned, answer ["N/A" ]. Only choose one of the following: ["immediate" ], ["short-term" ],["long-term" ], or ["N/A" ]. Example answer: ["long-term" ] User Input: FINDINGS: {findings} IMPRESSION: {impression} Prompt 7 o1-mini Scoring System Instruction: You are a radiology report comparison assistant. You will be given two lists of findings: one is the ground truth (GT), and the other is a candidate prediction (GEN). Your task is to compare them and return a similarity score between 0 and 1. 1. A score of 1.0 means they are clinically and semantically identical. 2. A score of 0.0 means they are completely different or unrelated. 3. Partial matches should get a score in between. Do not explain the score. Just output a float between 0 and 1. Example answer: </SCORE>"0.8"</SCORE> User Input: GT: {groundtruth} GEN: {candidate} o1-mini prompt 18 | https://arxiv.org/abs/2505.16325v1 |
arXiv:2505.16330v1 [cs.CL] 22 May 2025SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers Wenqing Wu, Chengzhi Zhang∗, Tong Bao, Yi Zhao aDepartment of Information Management,Nanjing University of Science and Technology, Nanjing, 210094, China Abstract Novelty is a core component of academic papers, and there are multiple perspectives on the assessment of novelty. Existing methods often focus on word or entity combinations, which provide limited insights. The content related to a paper’s novelty is typically distributed across different core sec- tions, e.g., Introduction, Methodology and Results. Therefore, exploring the optimal combination of sections for evaluating the novelty of a paper is important for advancing automated novelty assessment. In this paper, we utilize different combinations of sections from academic papers as inputs to drive language models to predict novelty scores. We then analyze the results to determine the optimal section combinations for novelty score prediction. We first employ natural language processing techniques to identify the sec- tional structure of academic papers, categorizing them into introduction, methods, results, and discussion (IMRaD). Subsequently, we used different combinations of these sections (e.g., introduction and methods) as inputs for pretrained language models (PLMs) and large language models (LLMs), em- ploying novelty scores provided by human expert reviewers as ground truth labels to obtain prediction results. The results indicate that using introduc- tion, results and discussion is most appropriate for assessing the novelty of a paper, while the use of the entire text does not yield significant results. Furthermore, based on the results of the PLMs and LLMs, the introduction and results appear to be the most important section for the task of novelty ∗Corresponding author Email addresses: winchywwq@njust.edu.cn (Wenqing Wu), zhangcz@njust.edu.cn (Chengzhi Zhang ), tbao@njust.edu.cn (Tong Bao), yizhao93@njust.edu.cn (Yi Zhao) score prediction. The code and dataset for this paper can be accessed at https://github.com/njust-winchy/SC4ANM. Keywords: Novelty score prediction, Large language model, Section structure combination, Pre-trained language model 1. Introduction Novelty is one of the core criteria in academic research, as it drives the frontier of knowledge, addresses unresolved questions in existing studies, or presents new insights. The current mainstream approach for evaluating the novelty of a paper utilizes the perspective of combinatorial innovation (Mat- sumoto et al., 2021; Wang et al., 2017; Uzzi et al., 2013), examining the distribution of references within the paper or the distribution of knowledge elements (Jeon et al., 2023; Luo et al., 2022). However, although easy to understand and simple to use, the extent to which cited publications serve as sources of inspiration for an academic paper is not yet well understood (Tahamtan and Bornmann, 2018). In addition, currently most of the main content of academic papers is not well used to evaluate novelty, and most of them are evaluated in the form of knowledge entities, keywords or topic words in the abstract and title. Content or entities related to the novelty of a paper are typically distributed across various sections, and relying solely on the abstract or title cannot fully capture all the knowledge utilized in the paper. When evaluating the novelty of a paper, relying solely on entity | https://arxiv.org/abs/2505.16330v1 |
or word-level content is limited. Furthermore, in previous novelty measures based on entities, the entire document was treated as a window for entities, without considering the impact of different section and their combinations on the novelty measurement. To address this limitation, it is essential to explore which sections or combinations of sections in a paper provide the most insightful information for an accurate automatic novelty evaluation. Generally, academic papers are divided into title, abstract, main text, and references. The main text can further be divided into introduction, meth- ods, results, and discussion (IMRaD) (Sollaci and Pereira, 2004). Although not all academic papers follow this structure, natural language processing techniques (Cohan et al., 2018a) can be employed to segment the content of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¶VQLFHWRVHHORZOHYHOIHDWXUHVOLNHHGJHV ³WKHWRSERXQGDULHVRIKRUL]RQWDOREMHFWV´ PLGGOHOHYHOIHDWXUHVOLNHVKDSHV ³3ROHVDQG OHJV´ DQGUHODWLYHO\KLJKOHYHOIHDWXUHVOLNHREM HFWV ³GRJIDFHV´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igure 1: An Example of review report on ICLR 2022. (https://openreview.net/forum?id=ltM1RMZntpu) the main text into these sections. In the peer review process1, the evaluation 1https://www.nature.com/nature/for-referees/how-to-write-a-report. https://2023.aclweb.org/blog/review-acl23/. https://icml.cc/Conferences/2023/ReviewerTutorial. 3 of an academic paper’s novelty primarily relies on the judgment of expert re- viewers. Reviewers typically do not base their assessment solely on the words or entities form the paper, but rather make their assessment after read the main text of the paper. Given that novelty often arises from the broader context and argumentation presented throughout the paper, it is crucial to understand how various sections contribute to the overall assessment. Con- sequently, simulating the review process by analyzing different combinations of paper sections can provide a more accurate prediction of a paper’s novelty. With advancements in natural language processing, traditional machine learning and deep learning technology (Darraz et al., 2024; Zhu et al., 2025), particularly the emergence of large language models (LLMs) (OpenAI, 2024; Meta, 2024), the ability of machines to process academic papers has been significantly enhanced. LLMs have also demonstrated commendable perfor- mance in research related to peer review (Liang et al., 2023; Zhou et al., 2024), although there remains a considerable gap compared to human re- viewers. Current research using LLMs primarily focuses on the macro-level evaluation of academic papers. However, the assessment of the novelty of papers is often overlooked, lacking clear evaluation or validation. With an increasing number of conferences, journals, and platforms such as NeurIPS2, eLife3, PeerJ4, OpenReview5, and F1000Research6offering open peer review, the novelty scores provided by expert reviewers can be obtained from these publicly available peer review reports, | https://arxiv.org/abs/2505.16330v1 |
as illustrated in the red box in Figure 1. The novelty scores provided in the reviewers’ reports can serve as the evaluation standard for a paper’s novelty. Currently, a research (Wu et al., 2024) have been conducted using open peer review reports to analyze the consistency of text scores. We believe that the novelty scores given by reviewers can serve as a reference standard for evaluating the novelty of a paper. To identify and analyze the optimal sections or combinations of sections for evaluating the novelty of academic papers, we obtained all peer review https://iclr.cc/Conferences/2024/ReviewerGuide. These reviewer report instructions clearly require reviewers to evaluate or grade the significance and novelty of the paper. 2https://neurips.cc/Conferences/2022/CallForPapers 3https://reviewer.elifesciences.org/author-guide/editorial-process 4https://peerj.com/benefits/review-history-and-peer-review/ 5https://openreview.net/about 6https://f1000research.com/about 4 reports and submitted paper PDFs from the ICLR 2022 and ICLR 2023 conferences on the OpenReview platform. These peer review reports require reviewers to provide novelty scores for the papers. Based on this dataset, we conducted an empirical study on predicting the novelty of papers using dif- ferent combinations of academic paper sections. Additionally, we validated the performance of LLMs on this task. The study is driven by the following three research questions. RQ1 : Which combinations of sections yield novelty scores that most closely align with the ground truth scores? By identifying which combina- tions of sections yield prediction scores closest to the actual novelty scores, we can assess the model’s accuracy. This means that, based on these com- binations, the model can predict results that more closely align with the true novelty scores of a paper, thereby reducing bias and errors. Addition- ally, this approach helps validate the model’s effectiveness under different combinations, revealing which configurations best replicate human reviewer judgments, thus ensuring the model’s rationality and credibility. RQ2 : Which combinations of sections are most effective in predicting the novelty scores of academic papers? Different sections of a paper (such as Introduction, Methods, Results) may have varying impacts on novelty scores. Understanding how these sections can be combined to produce the most ac- curate predictions not only aids in improving novelty scoring algorithms but also provides deeper insights into the relationship between paper structure and content. RQ3 : Which combinations of sections should be prioritized when au- tomatically evaluating the novelty of a paper? For large-scale automated review systems, it is crucial to prioritize which sections of a paper should be focused on during the automatic evaluation process, as this can enhance both the efficiency and accuracy of the review. By prioritizing the sections most representative of novelty, the influence of subjective human factors can be minimized, making the evaluation more objective and fairer. The main contributions of this paper are reflected in the following three aspects. Firstly, we used the novelty scores from peer reviews as a benchmark to fine-tune current popular pre-trained language models (PLMs) designed for long texts to predict novelty scores. We also validated the effectiveness of using different section structures as input. Secondly, we conducted a small-scale test on novelty score prediction us- ing prompt-based methods on LLMs. Furthermore, we analyzed the perfor- 5 mance | https://arxiv.org/abs/2505.16330v1 |
of the LLMs in this task under different chapter combinations, as well as the consistency between generated novelty scores and grounded scores. Thirdly, the results indicate that fine-tuned PLMs outperform LLMs in predicting novelty scores, though their performance is not yet satisfactory. Furthermore, our findings suggest that the introduction, results, and dis- cussion sections are more beneficial for automatic novelty score prediction tasks. All the data and source code of this paper are freely available at the GitHub website: https://github.com/njust-winchy/SC4ANM. 2. Related work In this section, we will report related work about our research. Firstly, we present relevant work in the field of identification of academic paper struc- ture. Subsequently, we delve into literature concerning LLMs for reviewing, followed by an overview of studies investigating novelty assessment methods. 2.1. Novelty and other relevant concepts To formally define novelty, we first clarify the distinctions between novelty and other related concepts, such as innovation, disruptiveness, and original- ity. In the academic community, novelty is currently defined in several ways. One definition (Foster et al., 2021) defines novelty as the degree of difference between a new scientific article and the existing body of scientific literature. Another definition (Arts et al., 2021) defines novelty as the uniqueness of specific knowledge elements, where the inclusion of new knowledge elements in a scientific article indicates that it conveys novel information. In addition, there is another definition (Boudreau et al., 2016) considers novelty as the result of a new combination of knowledge elements. Overall, the novelty of scientific articles can be unified as the quality of presenting new information within these articles. Innovation (Rogers, 1998) can be defined as the transformation of new ideas into practical, valuable, or sustainable outcomes. Unlike mere novelty, innovation must also generate impact in practice. Novelty is merely the first step toward innovation (Runco and Jaeger, 2012), which further requires the realization of practicality, impact, and value. Novelty can emerge in various settings, such as universities, whereas innovation primarily takes place within firms operating in the commercial sector (Fagerberg, 2006). 6 Disruptiveness (Funk and Owen-Smith, 2017) refers to research or discov- eries that fundamentally alter existing academic paradigms, research meth- ods, or core theories within a field. This change not only impacts academic discourse but may also influence societal, industrial, and even policy-level transformations. Disruptiveness (Wang, 2024) is often driven by research out- comes that break conventional norms and propose revolutionary new ideas. In contrast to novelty, disruptiveness not only emphasizes the origins of scien- tific research but also considers its utility and impact (Leibel and Bornmann, 2024). Originality (Shibayama and Wang, 2020; Hou et al., 2022) can be defined as the generation of new ideas, methods, conclusions, and other valuable outputs that depart from existing knowledge, or as a catalyst for further in- novation. In practical measurement, distinguishing between originality and novelty is often challenging, as originality is typically implicit in research papers (Guetzkow et al., 2004). Consequently, in most cases, originality and novelty are frequently used interchangeably (Uzzi et al., 2013; Shibayama and Wang, 2020; Wang, 2024). In summary, novelty forms the | https://arxiv.org/abs/2505.16330v1 |
foundation of both innovation and disrup- tiveness and is synonymous with originality. Without novel ideas or discov- eries, it is impossible to develop innovative or disruptive outcomes. While scholars may offer varying interpretations of novelty, there is a consensus that in scientific papers, novelty refers to the intrinsic quality of presenting new knowledge. Specifically, a novel scientific paper addresses new research questions, methodologies, results, theories, or the reorganization of exist- ing knowledge elements. Therefore, in this paper, we explore the prediction of novelty scores from the perspective of different combinations of sections. Specifically, various sections of a scientific paper, such as the Introduction, Methods, Results, and Discussion, often carry different types of information, which play a crucial role in presenting novelty. 2.2. Novelty measurements of scientific publications Novelty is one of the key criteria for evaluating the quality of academic pa- pers. In the current community of scholars, novelty is defined as the reor- ganization of existing knowledge components in an unprecedented manner (Schumpeter, 2006; Nelson, 1985). Currently, scholars assess the novelty of a study by evaluating the new combinations of references, considering that knowledge reorganization is based on the content of the references section. Uzzi et al. (Uzzi et al., 2013) analyzed 17.9 million papers on Web of Sci- 7 ence to study the relationship between reference combinations and citation counts. They found that the most impactful papers combine typical earlier works with new, unique combinations. This discovery helps distinguish be- tween novel and less novel papers. Wang et al. (Wang et al., 2017) explored the relationship between the novelty of scientific research, defined by first- time combinations of referenced journals, and its long-term impact. They find that highly novel research offers significant benefits but also involves higher risks, often faces delayed recognition, and is published in lower Im- pact Factor journals. Matsumoto et al. (Matsumoto et al., 2021) applied a novelty indicator to quantify the reference similarity between a focal paper and pre-existing papers within the same domain across various fields of natu- ral sciences. They proposed a new method for identifying papers belonging to the same domain as the focal paper using only bibliometric data. Shibayama et al. (Shibayama et al., 2021) developed a more integrated method that utilizes both references and the content of a paper. In their approach, they quantify the semantic distance of references to assess the novelty of a given paper. However, although the reference is easy to understand and use, it remains unclear to what extent they can serve as a source of inspiration for academic papers (Tahamtan and Bornmann, 2018). Additionally, references in papers are provided solely by the authors themselves, without any oversight mech- anism to ensure their quality. Recently, methods for discovering innovative content based on text analysis have been gaining popularity. Luo et al. (Luo et al., 2022) introduced a novel approach to measuring the novelty of pa- pers from the perspective of problem method combinations. They proposed a semantic novelty measurement algorithm based on term semantic similar- ity, and evaluated the effectiveness of the method through case studies and | https://arxiv.org/abs/2505.16330v1 |
statistical analysis. Yin et al. (Yin et al., 2023) developed a word embed- ding model using machine learning to extract semantic information related to elements of knowledge innovation from textual data. Jeon et al. (Jeon et al., 2023) proposed an analytical framework that uses paper titles to mea- sure the novelty of scientific publications. Chen et al. (Chen et al., 2024) conducted an in-depth investigation into the relationship between the insti- tutional composition of author teams and the novelty of academic papers, using fine-grained knowledge entities to measure the novelty of the papers. Through case studies, they demonstrated that this framework is a useful supplementary tool. Liu et al. (Liu et al., 2024) explored the pursuit of sci- entific novelty in doctoral dissertations by Ph.D. students and evaluated the 8 gender-related differences in this process. Wang et al. (Wang et al., 2024) proposed MNSA-ITMCM framework integrates topic modeling and a cloud model to measure novelty in scientific articles by combining semantically in- formed topics and quantifying novelty to improve accuracy, demonstrated through empirical evaluations in biomedical and computer science domains. The aforementioned research methods have demonstrated their effective- ness to some extent, indicating that text analysis-based approaches can be used to evaluate novelty. However, these methods only utilized parts of the paper, such as titles, abstracts, or knowledge entities, for their assessments and did not consider the section content of the paper. The novelty of a pa- per cannot be accurately determined based on brief excerpts alone. During the peer review process, reviewers typically assess the novelty of a paper by reading its entire content or most of its sections. Therefore, it is crucial to explore which sections should be given priority when evaluating the novelty of a paper. This paper simulates human reading of different sections us- ing artificial intelligence and uses the novelty scores provided by reviewers as a benchmark to investigate the importance of various sectional content combinations. 2.3. Large Language Models for Reviewing With the rapid advancement of artificial intelligence and natural language processing technologies, LLMs (Brown et al., 2020; Ouyang et al., 2022), par- ticularly those supported by transformer-based architectures and pre-trained on massive datasets, have gradually come into the public eye. With the suc- cessive releases of ChatGPT and GPT-4 (OpenAI, 2024) by OpenAI, as well as Llama (Touvron et al., 2023a) and others (Meta, 2024; Touvron et al., 2023b; Chowdhery et al., 2023), these LLMs have demonstrated powerful language generation and understanding capabilities, attracting significant research interest from scholars in the academic community. (Patsakis et al., 2024; Shafee et al., 2024; Caruccio et al., 2024) Recently, research on using LLMs for peer review has been gaining in- creasing popularity. Liang et al. (Liang et al., 2023) conducted a large-scale empirical analysis to assess comments generated by GPT-4: they tag sev- eral metrics and investigated user satisfaction to measure comment quality. They found that while LLM-generated reviews were helpful, they might be non-generic and tended to focus on certain aspects of scientific feedback. Liu and Shah (Liu and Shah, 2023) validated the utility of LLMs across | https://arxiv.org/abs/2505.16330v1 |
three tasks: identifying errors, verifying checklists, and choosing the ”better” pa- 9 per. They concluded that LLMs serve well as review assistants for specific reviewing tasks; however, they are not yet sufficient for conducting compre- hensive evaluations of papers. Mike Thelwall (Thelwall, 2024) used GPT-4 to evaluate the quality of journal articles on a paper assessment dataset to test its effectiveness. The results indicated that GPT-4 appears to be in- sufficiently accurate for any formal or informal research quality assessment tasks. Zhou et al. (Zhou et al., 2024) conducted a comprehensive evaluation to determine whether LLMs can be qualified and reliable reviewers. They concluded that it is premature for LLMs to serve as automated scientific paper reviewers. Although there is potential for obtaining useful and accu- rate results, their current capabilities are not yet reliable enough. Robertson (Robertson, 2023) conducted a preliminary study on using GPT-4 to assist in the peer review process, providing initial evidence that artificial intelli- gence can effectively facilitate this process. Gao et al. (Gao et al., 2024) proposed an efficient two-stage review generation framework REVIEWER2 that simulates the distribution of potential aspects a review might address. Additionally, they generated a large-scale peer review dataset comprising 27,000 papers and 99,000 reviews based on this framework. However, these studies are all macro-level investigations, focusing on the overall evaluation of academic papers. Our research, in contrast, focuses specifically on the novelty of academic papers and examines the ability of LLMs to assess the novelty of academic papers. 2.4. Identification of Academic Paper Structure The IMRaD model is a classical system, proposed earlier and widely used in scientific literature (Sollaci and Pereira, 2004; Nair and Nair, 2014). It di- vides the structural functions of academic articles into four parts: Introduc- tion, Methods, Results, and Discussion. The purpose of identifying the sec- tional structure of academic papers is to determine their structural functions, specifically, to classify text segments (sentences, paragraphs, or sections) into their respective functional categories. Lu et al. (Lu et al., 2018) proposed a clustering method based on domain-specific structures using high-frequency section headings in scientific documents to automatically identify the struc- ture of scientific literature. They applied the proposed method to two tasks: academic search and keyword extraction, achieving good performance. Li et al. (Li and Wang, 2021) proposed a hybrid model that considers both sec- tion headings and the main text to automatically identify general sections in 10 academic literature. Ma et al. (Ma et al., 2022) utilized section headings to identify the structural functions of academic articles, incorporating relative position information and contextual information. The aforementioned studies have made significant progress in the task of sectional structure identification. Subsequent research on this topic has generally included relevant applications. Cohan et al. (Cohan et al., 2018b) proposed a novel hierarchical encoder for modeling the discourse structure of documents, applied across two large scientific paper datasets. Ji et al. (Ji et al., 2019) proposed using deep learning to automatically identify the functional structure of academic texts based on section content, laying the groundwork for research on the distribution of | https://arxiv.org/abs/2505.16330v1 |
reference locations. Qin et al. (Qin and Zhang, 2023) explored which sections of academic articles reviewers focus on most by analyzing the sectional structure, as well as identifying the specific content that reviewers pay attention to. Although the above research has done a lot of work on identifying the structure of academic papers, we believe that identifying the structure of academic papers is still a challenging task. Zhou and Li (Zhou and Li, 2020) investigated the problem of section identification in academic papers within the context of Chinese medical lit- erature. They employed effective features from classical machine learning algorithms to address this issue. In our approach, we use deep learning models and LLMs to identify the sectional structure, and we perform consistency checks between their results to ensure the accuracy of the identification process. 3. Methodology In this section, we will introduce our data collection and preprocessing pro- cess, as well as the novelty score prediction task. Subsequently, we will provide a detailed explanation of the methods applied for section structure identification and novelty score prediction. The following is the research method of this paper. Figure 2 provides an overview of the methodology used in this study, which includes Section Structure Identification, Fine tuning PLMs for novelty score prediction, and Generate novelty score prediction using LLM. 3.1. Data collection and preprocessing We obtained our peer review data from the OpenReview platform. The International Conference on Learning Representations (ICLR) is a premier 11 PubMed Dataset Arxiv DatasetFine tuned SciBERT Llama3prompt(a) Section Structure Identification Parsed pdfsPrediction Model Input: LLMsGenerate Novelty Score (c) Generate novelty score prediction using LLM I M RI M RInput:Fine tune PLMsPredict Novelty Score (b) Fine tun ed PLMs on novelty score prediction I M RI M RIR I IM D ID D IM D IM D IR R IR R D D D D R D R D I D I DManual correctionIM D IM D IM D IM D IM D IM D IM D IM DFinal Results Classification Results Classification value >0.8 promptConsistent classification results Identified section structuresIdentified section structuresLabel: Introduction Label: Methods Label: Results Label: DiscussionContent 1Tagged data Content 2 Content 3 Content 4Label: Introduction Label: Methods Label: Results Label: DiscussionContent 1Tagged data Content 2 Content 3 Content 4 Main text Paragraph 1 Paragraph 2... Paragraph nParagraph 1 Paragraph 2... Paragraph nInputNote : The combination of section structure takes the introduction (I), methods (M), and results (R) as an example. The green boxes represents the text of the corresponding section that has been identified. The blue boxes indicate that the PLM’s predicted classification value for the text is greater than 0.8. The orange boxes represent cases where the PLM’s classification result is less than 0.8, but the LLM’s prediction is consistent with it. The yellow boxs indicate that PLM’s predicted classification value for the text is less than 0.8 and inconsistent with the LLM, require human correction. Red boxs are the final result. Figure 2: Framework of this study. conference in the field of machine learning. We wrote a web crawler code to retrieve a total | https://arxiv.org/abs/2505.16330v1 |
of 8183 ICLR papers from ICLR 2022 and 2023, each con- taining peer review comments. The reason for selecting papers from these two years as the data source is that the review reports for these years require reviewers to provide novelty scores, as shown in Figure 1. Additionally, an- other reason for selecting data from these two years as the source for this study is that the common section types in ICLR papers closely align with the IMRD structure typically found in natural sciences. In the data we col- lected, all ICLR papers include an introduction, the section corresponding to the methods is generally labeled as ”Methodology” or the specific name 12 of the proposed method, the section corresponding to the results is usually related to experiments or model comparisons, and the section correspond- ing to the discussion typically includes analysis, discussion, and conclusions. Therefore, we can map these sections to the corresponding IMRD structure using a section structure recognition method in Section 3.3. It is also worth noting that the methods proposed in this paper are applicable in any domain where the sections of a paper can be classified. The peer review reports for ICLR 2022 and 2023 included the review text, Correctness, Technical Novelty and Significance, Empirical Novelty and Significance, Recommendation, and Confidence. Among these, the distribu- tion of Technical Novelty and Significance (TNS) scores ranges from 1 to 4, denoted as 1: The contributions are neither significant nor novel, 2: The contributions are only marginally significant or novel, 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work and 4: The contributions are significant, and do not exist in prior works, respectively. As ICLR is a conference in the field of computer science, we have chosen TNS as the criterion for predicting novelty scores. Each paper receives evaluations from at least three reviewers, and each reviewer assigns a TNS score. To obtain labels suitable for model training, we aggregate the scores provided by each reviewer by summing them up and then dividing by the total number of reviewers. However, discrepancies may arise when multiple reviewers have differing opinions, such as one assigning a score of 4 and another a score of 1. To address this issue, we identified the maximum and minimum scores in each review report and exclude instances where the difference exceeds 1, signifying reviewer disagreement. These cases are sub- sequently removed from the dataset. After the consistency processing of the scores mentioned above, there are ultimately 6,094 papers with peer review reports that have consistent TNS scores. Furthermore, to parse the acquired academic paper PDFs, we utilized the GROBID (2008-2022)7and S2ORC (Lo et al., 2020) tools to parse the PDFs into JSON format. In the large-scale dataset constructed by Gao et al. (Gao et al., 2024), the parsed content data of papers from ICLR 2022 and 2023 is included, eliminating the need for custom rules to extract paper content from JSON files. We matched the parsed paper abstracts with the dataset created by Gao et al. (Gao et al., 2024) | https://arxiv.org/abs/2505.16330v1 |
and ultimately matched 7https://github.com/kermitt2/grobid 13 Table 1: Statistical results of the novelty score data. TNSDecisionAccept Reject Total # TNS=1 0 60 60 # TNS=2 272 1726 1998 # TNS=3 750 599 1349 # TNS=4 87 6 93 # Papers 1109 2391 3500 Note: TNS is Technical Novelty and Significance score. the content of 3,500 papers. We investigated the reason for the significant reduction in the number of papers and found that many PDF files were non- editable, meaning they could not be parsed, or the matching process failed. The final distribution of novelty scores is shown in Table 1. From the Table 1, it is evident that the counts for scores 1 and 4 are relatively low. Pre- dicting directly with such an extremely imbalanced label distribution may result in biased prediction outcomes. Therefore, to address this issue and considering the importance of a novelty score of 4, we reclassified the scores by combining scores 1 and 2, retaining score 3 and score 4 as a separate cat- egory. According to the descriptions associated with each score, the novelty of paper is classified into three categories: 0: basic novel, 1: moderate novel, 2: highly novel. 3.2. Problem definition In this work, we introduce a new task called optimal section combinations for automated novelty measurement (SC4ANM). The objective of SC4ANM is to predict the novelty scores of academic papers based on varying combinations of their sections. The task aims to evaluate how various combinations of a paper’s sections impact to its novelty score. Formally, given an academic paper, let P= (S1, S2, ..., S n) represent the set of sections of the paper, where each section corresponds to a part of the paper (e.g., Introduction, Methods, Results, Discussion, etc.). A section combination Cis defined as a subset of these sections, C⊆Pselected for analysis. For example, combinations such as Introduction and Methods, Introduction and Results, or even Introduction only are valid section combinations. A set of corresponding labels L={l0: 0, l1: 1, l2: 2}represents the novelty scores defined in the end of Section 14 3.1. The goal of SC4ANM is to develop a classification model fthat assigns predefined novelty scores l{0,1,2}based on the content of different section combinations selected. 3.3. Section Structure Identification Firstly, we need to identify the structure of the parsed paper, dividing the main text into introduction, methods, results, and discussion. However, not all academic papers adhere to the IMRaD structure, thus necessitating the identification of section structures in the parsed PDF con- tent. The parsed paper data not only includes the main text of the papers but also contains some additional content, such as page number and page header, quotes or excerpts etc. Therefore, it is necessary to identify the main text of the papers to remove any extraneous information. We utilized an academic text classifier, fine-tuned on linguistic academic publications8, as the identification model to recognize the main content in our paper parsed data. We saved the extracted main text of academic papers in the form of paragraphs or sentences. Then, we perform section structure identification on the extracted | https://arxiv.org/abs/2505.16330v1 |
main <Main text of academic papers > This is an academic text, which could be an introduction, methods, results or discussion. Please reply which section it pertains to: introduction, methods, results or discussion.Prompt: Figure 3: Prompt of Llama 3 for section structure identification. text. Cohan et al. (Cohan et al., 2018b) provided two datasets of scientific papers. The datasets are sourced from the ArXiv and PubMed OpenAccess repositories, both of which provide the names of sections and their corre- sponding content. We extracted all IMRaD section content from the two 8https://huggingface.co/howanching-clara/classifier foracademic texts 15 datasets , totaling 300,000 entries, and divided them into training, valida- tion, and test sets in an 8:1:1 ratio. We then fine-tuned the SciBERT (Beltagy et al., 2019) on this data to train a four-class classifier aimed at determining which section structure a given piece of academic text belongs to. The final model achieved a classification accuracy rate of 91%. We believe that relying solely on the fine-tuned PLM does not achieve the best recognition results. Therefore, we also used LLaMA3 (Meta, 2024) for secondary recognition of the extracted main text. The specific prompt is shown in Figure 3, the con- tent within angle brackets represents the main text of the paper that require section structure recognition. If the predicted value for a given class from the PLM exceeds 0.8, we consider the prediction to be correct. Otherwise, further validation of the classification result is required. The threshold is set to 0.8 because we consider section structure identification to be a crucial task that should prioritize minimizing false positives. In other words, it is preferable to conduct further validation rather than risk incorrectly classifying sections as correct. Based on this application requirement, we believe that setting the threshold to 0.8 is a reasonable choice. We employed LLama3 for secondary validation. When the predictions from the trained model are consistent with the results from LLaMA3, we consider the prediction to be correct. When the predictions are inconsistent, we save the text and manually identify the section structure it belongs to. At this point, we have obtained the identified section structures and their corresponding content for each paper. 3.4. Fine tuning PLMs for novelty score prediction Paper content Introduction Methods Results DiscussionTitle Abstract Introduction Methods Section Combination sLongFormerText embeddingMLPPrediction Figure 4: An Example of Fine-tuning PLMs for Novelty Score Prediction. Using Longformer to represent the PLM and Introduction + Methods to represent the section structure combinations as examples. We fine-tuned five PLMs designed for handling long texts to accomplish the task of novelty score prediction. These models include Longformer (Belt- 16 agy et al., 2020), BigBird (Zaheer et al., 2020), LongT5 (Guo et al., 2022), LED (Longformer Encoder-Decoder) (Shen et al., 2023), and the Longformer version of SciBERT9. We divided the academic paper data, with identified section structures, into training, validation, and test sets in an 8:1:1 ratio, with 2500, 350, and 350, respectively. Next, as illustrated in the lower left corner of Figure 2, we input different combinations of section structures (such as “Title+Abstract”, “Introduction”, “Introduction+Methods”, etc., total- ing | https://arxiv.org/abs/2505.16330v1 |
16 combinations) to fine-tuned the PLMs. The results are then fed into an MLP to obtain the final prediction, which is the three-class classification of the novelty score. The detailed process is shown in Figure 4. 3.5. Generate novelty score prediction using LLM Note: The number of section combinations ranges from 1 to 4; here, we use three section combinations as an example.You are a helpful assistant. You need to perform a text classification task with three labels [0: basic novel, 1: moderate novel, 2: highly novel]. The following is an academic paper text, assign corresponding labels to it: <Section1 name, Section1 content> <Section2 name, Section2 content> <Section3 name, Section3 content> Just return the label number. Figure 5: An Example used to prompt the large language model for the novelty score prediction task. In addition to fine-tuning PLMs for novelty score prediction, we also tested the performance of LLMs in predicting novelty scores under different sec- tion structure combinations. From the data statistics in Table 1, it can be observed that the number of papers with a novelty score of 4 is relatively low. Therefore, we randomly selected 40 papers with a novelty score of 4 as the experimental subjects. Additionally, the number of papers with other scores was made equal to that of the score 4 papers, also selected randomly. Notably, to ensure the diversity of experimental data, we performed ran- dom sampling of the papers again for each different combination of section structures used as input. For the selection of LLMs, we chose GPT-3.5 and 9https://huggingface.co/yorko/scibert scivocab uncased long 4096 17 GPT-4o as the models for our experiments. By calling their APIs and us- ing prompt-based learning, we obtained predictions for the novelty scores of academic papers. The specific prompt is shown in Figure 5. It is impor- tant to note that although we instructed the model to return only numerical classification labels in the prompt, GPT-3.5 still generated some additional content. Consequently, we performed a secondary verification of GPT-3.5’s outputs. GPT-4o did not exhibit this issue. 4. Result analysis In this section, we present the results of predicting novelty scores using dif- ferent section structures and explore the following three research questions based on these results. 4.1. Evaluation Metrics on the SC4ANM task. The Accuracy (Acc) and F1-score were selected to evaluate classification performance. The formulas used are as follows: Accuracy =PTPi The total number of sample(1) Where TPiis the number of correct predictions for category i(0: basic novel, 1: moderate novel, 2: highly novel). The calculation method for the F1score of each category iis as follows: Precision i=TPi TPi+FPi(2) Recall i=TPi TPi+FNi(3) F1i= 2×Precision i×Recall i Precision i+Recall i(4) Where irepresents the category index (0, 1, 2), TPiis the number of correct predictions for category i,FPiis the number of incorrectly predicted as cat- egory ibut does not actually belong to class i,FNirepresents the number of instances that actually belong to category ibut are incorrectly predicted as another category. Since the weighted average F1score is calculated by weighting each cate- gory according to its sample size, it is more | https://arxiv.org/abs/2505.16330v1 |
suitable for imbalanced datasets. 18 Therefore, we utilize the weighted average F1score, the weight for each cat- egory i: Weighted F1=Xci c×F1i (5) Where cirepresents the number of instances for category i,cdenotes the total number of samples, F1iis the F1score for category i. In addition to utilizing common metrics for evaluating classification per- formance, we also employed the Pearson, Spearman and Kendall’s tau cor- relation coefficients, which are typically used to assess the score prediction capability of models. The formula for calculating the Pearson correlation coefficient is as follows: r=Cov(x, y) σxσy(6) Where Cov is covariance, σis standard deviation. The Spearman formula is applied: rs= 1−6nd2 n2−1(7) d2=1 nnX i=1d2 i (8) di=rx(i)−ry(i) (9) Where rx(i) and ry(i) represent the rank of xiandyi,nis the total number of samples. The formula for Kendall’s Tau correlation coefficient is as follows: τ=2(C−D) n(n−1)(10) Where Crepresents the number of concordant category pairs (i.e., when xi> x jandyi> y j, orxi< x jandyi< y j),Drepresents the number of discordant category pairs (i.e., when xi> x jandyi< y j, orxi< x jand yi> yj),nis the total number of samples. 4.2. Traditional novelty measurement methods as baselines We chose the novelty measurement method proposed by Shibayama et al. (Shibayama et al., 2021) as the representative of traditional approaches for 19 comparison as the baseline. Their method calculates the novelty score by determining the semantic distance between the word vectors of the paper’s title and those of the references’ titles. We then simply present their novelty calculation method. First to calculate the distance between each pair of cited documents, the cosine distance between i−thandj−threferences(1 ≤i≤ j≤N) is given by: dij= 1−vi·vj |vi||vj|(11) Where vis mean of word embeddings of all words included, Nis number of the reference. Then calculate the distance scores( Novel q): Novel q=R−1Nq 100 (12) Where R(dij)is the ordinal rank of dijof all the distances of N(N−1)/2 reference pairs, qrepresents the percentile rank of the distance score, with a range of [0,100]. According to the findings in Shibayama et al.’s(Shibayama et al., 2021) study, setting qto 100 yields the best results; therefore, we also setqto 100. Since the computed results Novel qfall within the range [0,1], we apply a linear scaling method to map the results to the range [0,2] and round them to the nearest integer: scaled value= 2×Novel q−min(value list) max(value list)−min(value list)(13) Where value listis the list of all calculated distance scores Novel q, and min(value list) and max(value list) are the minimum and maximum values in the list, respectively. 4.3. Correlation test on novelty score prediction with different section com- binations We will answer RQ1 by examining the correlation between the prediction and the ground truth in this section. 4.3.1. Correlation analysis between prediction by PLMs and ground truth In addition to the traditional metrics of accuracy and absolute differ- ence for the score prediction task, we also calculate Pearson, Spearman, and Kendall’s tau correlation coefficient. These three-correlation coefficient met- rics are commonly used to evaluate the score prediction capabilities of models, 20 as demonstrated in studies on using LLMs to evaluate abstractive | https://arxiv.org/abs/2505.16330v1 |
summaries (Shen et al., 2023) and machine translations (Kocmi and Federmann, 2023). The result of correlation coefficient between PLMs prediction and ground truth is shown in Table 2. 21 Table 2: The result of the correlation coefficient between the PLMs’s prediction and the ground truth. SCLongformer BigBird LongT5 LED SciBERT P SP K P SP K P SP K P SP K P SP K T 0.0488 0.0602 0.0481 0.0491 0.0606 0.0483 0.0488 0.0603 0.0480 0.0491 0.0608 0.0485 0.0492 0.0610 0.0488 A 0.0890 0.0832 0.0879 0.0891 0.0835 0.0882 0.0892 0.0836 0.0882 0.0892 0.0836 0.0882 0.0894 0.0838 0.0884 TA 0 .1555b0.1503b0.1483b0.1656b0.1604b0.1594b0.1660b0.1659b0.1601b0.1662b0.1662b0.1651b0.1885b0.1885b0.1816b I 0 .2002c0.1976c0.1951c0.2021c0.2019c0.1975c0.2011c0.1990c0.1964c0.2020c0.1999c0.1970c0.2030c0.2010c0.1983c IM 0 .2150c0.2105c0.2077c0.2180c0.2134c0.2110c0.2161c0.2115c0.2080c0.2161c0.2115c0.2090c0.2199c0.2140c0.2122c IMR 0 .2811a0.2740a0.2730a0.2847a0.2781a0.2770a0.2811a0.2741a0.2733a0.2830a0.2761a0.2751a0.2867a0.2791a0.2785a IMD 0 .2830b0.2772b0.2741b0.2850b0.2781b0.2773b0.2859b0.2801b0.2777b0.2850b0.2783b0.2777b0.2881b0.2810b0.2804b IMRD 0 .2711c0.2750b0.2695b0.2721c0.2771b0.2710b0.2710c0.2755b0.2688b0.2730c0.2771b0.2710b0.2850c0.2889b0.2831b IR 0 .2170c0.2131c0.2100c0.2200c0.2165c0.2131c0.2183c0.2140c0.2111c0.2194c0.2150c0.2122c0.2222c0.2181c0.2155c IRD 0.3181c0.3080c0.3045c0.3205c0.3104c0.3060c0.3211c0.3110c0.3071c0.3201c0.3103c0.3061c0.3261c0.3154c0.3125c ID 0 .2774c0.2701c0.2671c0.2793c0.2721c0.2690c0.2801c0.2735c0.2701c0.2790c0.2721c0.2692c0.2810c0.2741c0.2711c M 0 .2090c0.2021c0.2000c0.2112c0.2041c0.2020c0.2101c0.2030c0.2010c0.2091c0.2020c0.2000c0.2130c0.2065c0.2043c MR 0.1767 0.1704 0.1701 0.1805 0.1744 0.1735 0.1821 0.1744 0.1743 0.1812 0.1734 0.1743 0.1854 0.1789 0.1778 MD 0 .1681a0.1612 0.1603 0 .1689a0.1621 0.1612 0 .1601a0.1630 0.1621 0 .1688a0.1621 0.1612 0 .1754a0.1681 0.1671 MRD 0 .1690a0.1621a0.1601a0.1712a0.1643a0.1621a0.1701a0.1634a0.1610a0.1721a0.1651a0.1634a0.1751a0.1683a0.1667a R 0 .1931c0.1761c0.1743a0.1951c0.1783c0.1761a0.1985c0.1818c0.1793b0.1961c0.1792c0.1774b0.1991c0.1834c0.1812b RD 0 .1312a0.1081a0.1067a0.1333a0.1102a0.1091a0.1321a0.1092a0.1083a0.1333a0.1110a0.1101a0.1362a0.1132a0.1110a D 0 .1901c0.1732c0.1711c0.1923c0.1741c0.1725c0.1912c0.1734c0.1721c0.1934c0.1762c0.1745c0.1962c0.1899c0.1783c Note: a represents p <0.05; b represents p <0.01; c represents p <0.001. SC, T, A, I, M, R, D respectively represent section combination, title, abstract, introduction, methods, results, and discussion. For example, TA means the model input consists of the title and abstract, with other letter combinations following the same pattern. SciBERT is Longformer version. P, SP and K is Pearson, Spearman and Kendall’s tau correlation coefficient, respectively. 22 From the results in the Table 2, we can be observed that the predicted novelty scores based on all section combinations positively correlate with the actual scores. We attribute this to the fact that various sections of an aca- demic paper, including the title and abstract, contain information that aids in predicting the novelty score. SciBERT achieved the highest correlation coefficient, which is closely related to its training corpus, enabling it to make relatively accurate predictions for novelty score prediction. Furthermore, we can observe that the prediction and true score correlation coefficients are highest when using combinations of three-section structures as input, espe- cially IRD or any combination that includes the introduction. This indicates that for the task of predicting novelty scores, combinations of three-section structures, particularly those including the introduction, are more effective. It is worth noting that while the correlation coefficient for IMRD is not the highest, it remains competitive with other high-accuracy combinations. This suggests that when the model uses IMRD as input, the information from various section structures can influence its judgment, leading to incorrect predictions. Although these predictions are inaccurate, the difference from the true scores is relatively small, resulting in lower accuracy but relatively higher correlation coefficients. 4.3.2. Correlation analysis between prediction by LLMs and ground truth To further evaluate the ability of LLMs in predicting novelty scores, we cal- culated three correlation coefficients between the generated predictions and the real scores. The detailed results are presented in Table 3. First, we analyzed the results of GPT-3.5. The results indicate that GPT-3.5’s predictions for many section combinations exhibit a negative cor- relation with the real scores. | https://arxiv.org/abs/2505.16330v1 |
This suggests that GPT-3.5 is not adept at the task of novelty score prediction, likely due to the hallucination problem inherent in LLMs. For the results generated by GPT-3.5, we need to further process many additional contents generated that is consistent with our re- quirements. This may also be the reason for the negative correlation in the correlation coefficient calculation results of GPT-3.5. Furthermore, we ob- serve that the correlation coefficients for the IMR and R section combinations are relatively high. This indicates that using a combination of three-section structures is relatively suitable for predicting novelty scores. Then, we analyze the results of GPT-4o. From the correlation coefficient calculations, we see that nearly all results exhibit a positive correlation. This indicates that GPT-4o is capable of understanding and performing the task 23 Table 3: The result of the correlation coefficient between the LLM’s prediction and the ground truth. SCGPT-3.5 GPT-4o P SP K P SP K T 0.0248 0.0250 0.0227 0.1035 0.1035 0.0976 A 0.1525 0.1566 0.1433 -0.0295 -0.1100 -0.0277 TA -0.1054 -0.1054 -0.0994 0.1581 0.1474 0.1381 I -0.0787 -0.0565 -0.0522 0.0192 0.0201 0.0199 IM -0.0674 -0.0453 -0.0416 0.0199 0.0318 0.0299 IMR 0.1550 0.1432 0.1328 0 .1916a0.1916a0.1806a IMD -0.1021 -0.1243 -0.1163 0.1602 0.1643 0.1536 IMRD -0.0815 -0.1240 -0.1160 0.1617 0.1789 0.1681 IR 0.0386 0.0420 0.0386 0 .1921a0.1896a0.1771a IRD -0.1129 -0.1002 -0.0936 0.0207 0.0207 0.0195 ID 0.0404 0.0721 0.0670 0.1038 0.1038 0.0979 M -0.1517 -0.1708 -0.1566 0.0545 0.0803 0.0744 MR 0.0809 0.0502 0.0456 0.0793 0.0808 0.0755 MD 0.0354 0.0499 0.0462 0.1090 0.1088 0.1003 MRD -0.1066 -0.1108 -0.1019 0.0779 0.0803 0.0755 R 0.1273 0.1603 0.1510 0.1527 0.1554 0.1460 RD 0.0185 0.0659 0.0612 0.2389a0.2329b0.2186a D 0.0748 0.0605 0.0564 0.0640 0.0536 0.0472 Note: a represents p <0.05; b represents p <0.01; c represents p <0.001. SC is Section Combination. P, SP and K is Pearson, Spearman and Kendall’s tau correlation coef- ficient, respectively. The other letter abbreviations are the same as Table 2. to a certain extent. Compared to the accuracy and F1 scores, the correlation coefficient results for GPT-4o predictions are more impressive. Unlike the PLMs, the section combinations that include the Results section show higher correlation coefficients. We believe this indicates that GPT-4o places greater emphasis on the impact of the Results section on novelty scores. Additionally, we think the Discussion section overlaps with the Introduction, which may explain why the IR and RD combinations outperform others. The poorer performance of the IRD combination is likely due to the confusion caused by the overlap between the Introduction and Discussion sections. Additionally, we can observe that the IMR and IMD combinations also exhibit competitive 24 results, indicating that the Methods section holds considerable significance as well. 4.4. Novelty score prediction performance with different section combinations We answer RQ2 by analyzing the performance of the PLMs and LLMs in the novelty score prediction task in this section. 25 Table 4: The results of different section combinations of PLMs in novelty score prediction tasks. SCLongformer BigBird LongT5 LED SciBERT Acc F1 Acc F1 Acc F1 Acc F1 Acc F1 T 0.5274 0.5168 0.5265 0.5157 0.5266 0.5159 0.5281 | https://arxiv.org/abs/2505.16330v1 |
0.5172 0.5283 0.5175 A 0.5331 0.5312 0.5334 0.5315 0.5332 0.5314 0.5335 0.5318 0.5338 0.5320 TA 0.5943 0.5880 0.5971 0.5900 0.5951 0.5891 0.5940 0.5891 0.5982 0.5901 I 0.6114 0.5863 0.6082 0.5811 0.6102 0.5843 0.6123 0.5861 0.6122 0.5854 IM 0.6114 0.5903 0.6130 0.5921 0.6122 0.5911 0.6112 0.5891 0.6152 0.5922 IMR 0.6577 0.6351 0.6550 0.6309 0.6574 0.6352 0.6562 0.6354 0.6600 0.6381 IMD 0.6572 0.6281 0.6541 0.6322 0.6567 0.6281 0.6573 0.6543 0.6592 0.6314 IMRD 0.6145 0.5940 0.6101 0.592 0.6167 0.5921 0.6143 0.5952 0.6182 0.5973 IR 0.6200 0.6143 0.6222 0.6154 0.6200 0.6152 0.6201 0.6152 0.6221 0.6152 IRD 0.6681 0.6455 0.6701 0.6503 0.6692 0.6481 0.6778 0.6462 0.6824 0.6515 ID 0.6515 0.6333 0.6534 0.6341 0.6563 0.6333 0.6512 0.6322 0.6532 0.6354 M 0.6000 0.5781 0.6010 0.5795 0.6021 0.5794 0.6014 0.5781 0.6031 0.5812 MR 0.5801 0.5601 0.5834 0.5622 0.5841 0.5643 0.5812 0.5589 0.5854 0.5632 MD 0.5861 0.5757 0.5861 0.5765 0.5885 0.5785 0.5867 0.5750 0.5902 0.5824 MRD 0.5931 0.5862 0.5951 0.5881 0.5961 0.5892 0.5900 0.5881 0.5973 0.5996 R 0.5820 0.5670 0.5834 0.5681 0.5887 0.5733 0.5824 0.5671 0.5912 0.5793 RD 0.5500 0.5341 0.5523 0.5364 0.5601 0.5410 0.5559 0.5342 0.5697 0.5512 D 0.5689 0.5650 0.5712 0.5662 0.5750 0.5681 0.5767 0.5681 0.5813 0.5667 Note: SC, T, A, I, M, R, D respectively represent section combination, title, abstract, introduction, methods, results, and discussion. For example, TA means the model input consists of the title and abstract, with other letter combinations following the same pattern. SciBERT is Longformer version. F1isWeighted F1. 26 4.4.1. Results of PLMs with different section combinations To address this research question, we fine-tuned five PLMs on the novelty score prediction task using different section combinations. The training pro- cess spanned 10 epochs with an initial learning rate of 0.0001, which gradually decreased. To prevent overfitting, we implemented an early stopping strat- egy, terminating training if the F1 score on the validation set did not improve for three consecutive epochs. The final results are shown in Table 4. As shown in Table 4, the performance of the five PLMs on the novelty score prediction task does not differ significantly. However, SciBERT slightly outperforms the other models, likely because it is trained on scientific-related corpora. While the other pre-trained models also include academic papers in their training data, their performance may be affected by the presence of other non-academic corpora. Furthermore, the results show that input com- binations including the introduction tend to perform better than other com- binations. We attribute this to the fact that authors typically articulate the contributions or innovative aspects of their papers in the introduction, which are relevant to assessing novelty. These details likely influence the model’s judgment of the novelty score. Moreover, considering the performance of each model, input combinations with three sections yield the highest accuracy and F1 scores, with the IRD combination performing the best, followed by IMR and IMD. We believe that while the introduction already contains significant information relevant to predicting novelty scores , such as the main contri- butions proposed by the author, the inclusion of additional sections further enhances the model’s understanding of the task, leading to improved results. Finally, we observe that | https://arxiv.org/abs/2505.16330v1 |
model performance of IMRD is not good. We think that the content and knowledge encompassed in the full text of a paper are too extensive for current PLMs to capture knowledge effectively for the task of novelty score prediction. Furthermore, we observed that the highest accu- racy achieved was only 0.682. Therefore, we conclude that the task of novelty score prediction poses a significant challenge for PLMs. 4.4.2. Result of novelty score prediction using LLM We tested the performance of two LLMs (GPT-3.5 and GPT-4o) on the task of predicting novelty scores based on different section combinations. All re- sults were obtained in the form of zero shot. To mitigate the randomness of the generated results, we conducted five predictions and selected the most frequently occurring score. The results are presented in Table 5. From the results, we can see that the novel score prediction task also 27 Table 5: The results of LLMs on the task of novelty score prediction. Section CombinationGPT-3.5 GPT-4o Acc F1 Acc F1 T 0.3333 0.2508 0.3083 0.2418 A 0.3500 0.2836 0.3333 0.2145 TA 0.3500 0.2803 0.3083 0.2230 I 0.2917 0.2220 0.3000 0.2493 IM 0.3167 0.2676 0.3500 0.2800 IMR 0.3917 0.3388 0.3417 0.2616 IMD 0.2833 0.2260 0.3167 0.2471 IMRD 0.3083 0.2841 0.3917 0.3088 IR 0.4000 0.3194 0.3833 0.3161 IRD 0.2583 0.1813 0.2917 0.2296 ID 0.3917 0.3097 0.3750 0.2970 M 0.2667 0.2351 0.3333 0.2744 MR 0.3417 0.3094 0.3000 0.2420 MD 0.3250 0.2701 0.3333 0.2902 MRD 0.3167 0.2587 0.3333 0.2786 R 0.3417 0.2815 0.3750 0.2912 RD 0.3250 0.2457 0.3833 0.3241 D 0.3500 0.3018 0.2833 0.2704 Note: The letter abbreviations are the same as Table 4. F1isWeighted F1. poses challenges for these two LLMs, with the highest accuracy only reach- ing 0.4. We attribute the suboptimal performance of the LLMs to several factors. Firstly, in a zero-shot scenario, LLMs struggle to grasp the patterns associated with novelty score prediction, leading to erroneous judgments. Secondly, the training corpus of these models may not include data relevant to the novelty score task. Thirdly, the models’ limited reasoning capabilities hinder their ability to infer the task content based on the provided prompts. From the perspective of different section combinations, the results for both GPT-3.5 and GPT-4o are quite unstable. GPT-3.5 performs better when the input is IR, while GPT-4o performs better with IMRD as the input. We believe this instability is related to the tendency of LLMs to generate favorable responses, as they are inclined to provide satisfactory answers or predictions. Finally, for the results generated from LLM, we examined the 28 generated content and found that LLM tends to give the highest novelty score (highly novel). We think this tendency contributes to the low accuracy and F1 scores. Table 6: The result of traditional method and best performance by our method. Method Acc F1 P SP K Shibayama’s method0.4265 0.3637 0.0132 0.0362 0.0346 SciBERT+IRD 0.6824 0.6515 0 .3261c0.3154c0.3125c Note: a represents p <0.05; b represents p <0.01; c represents p <0.001. The letter abbreviations are the same as Table 4. F1isWeighted F1. 4.5. Comparison with a traditional novelty evaluation method In this | https://arxiv.org/abs/2505.16330v1 |
section, we compare the best-performing section combinations (SciBERT+IRD) with a traditional novelty evaluation method mentioned in Section 4.2. The results are shown in Table 6. As we can observe, our method outperforms the traditional method in terms of both accuracy and F1score, as well as the results of the correlation calculations. This demon- strates that using comprehensive text information to assess a paper’s novelty is more effective than the traditional citation-based methods. Furthermore, the results indicate that the comprehensive text information indeed contains more content that contributes to novelty evaluation, which is also supported by the findings in Sections 4.3 and 4.4. This further underscores the potential of leveraging a broader range of textual data for novelty detection. 4.6. Section association analysis for automated paper novelty assessment In this section, we will comprehensively analyze the results of PLMs and LLMs to answer RQ3. Based on the results from the first two research questions, we can proceed to discuss the third research question. As seen in the results from Table 4 and Table 2, models that include the introduction section consistently per- form better than other combinations. Therefore, we think the introduction to be a crucial section for evaluating the novelty of a paper. Furthermore, we observe that the combination of the introduction and results sections also achieves notable performance, indicating that this combination is beneficial for evaluating the novelty of a paper. Although these combinations did not 29 perform well with the LLMs, Tables 5 and 3 show that inputs including the results section perform relatively well. Therefore, we also consider the results section to be essential for assessing the novelty of a paper. Furthermore, we observe that the combination of the introduction, results, and discussion sec- tions yields the best performance with the PLMs. Similarly, the combination of results and discussion also performs well with the LLMs. Based on this, we can infer that the discussion section serves as a valuable supplement to the assessment of a paper’s novelty, building on the information provided in the introduction and results sections. Although the methods section is a crucial section for evaluating a paper’s novelty, its text description is often abstract, and the content related to novelty can be challenging for machines to comprehend. Therefore, it may not be suitable for automated assessment of a paper’s novelty. We believe that evaluating the novelty of the methods section requires external knowledge for support, such as the opinions of peer review experts. In summary, we believe that the combination of the introduc- tion, results, and discussion sections is crucial for the automated assessment of a paper’s novelty. 4.7. Case study As shown in Figure 6 and 7, we conducted case studies on PLM (Longformer version of SciBERT) and LLM (GPT-4o) based on different section combi- nations inputs. We treat the model’s output and the ground truth as two rows in a matrix, with the top row representing the labels and the bottom row representing the predictions. The letter combinations above each matrix represent the abbreviations for different section structures: I stand for Intro- duction, M | https://arxiv.org/abs/2505.16330v1 |
for Methods, R for Results, D for Discussion, T for Title, and A for Abstract. For example, IM represents the combination of Introduction and Methods. When the true label is 0, the range of discrepancies is [ −2,0]; when the true label is 1, the range is [ −1,1]; and when the true label is 2, the range is [0 ,2]. For example, if the true label is 0 and the prediction is 2, the discrepancy is -2, resulting in a darker color in the plot. We randomly selected two papers from each of the three novelty score categories, making a total of six papers for case studies. First, we examined the predictions of the PLM under different section combinations. From the Figure 6, it is evident that the PLM struggles to accurately predict high nov- elty scores but can generally make correct predictions for medium and low 30 Note : The x axis represents the true labels, and the numbers in different colors within each plot indicate the difference between the prediction and true labels. The legend is on the right, with darker colors indicating a greater difference between the predicted labels and the true labels. Figure 6: Case study on PLM (Longformer version of SciBERT) for novelty score prediction using different paper sections as input. 31 novelty scores. Regarding the proximity of score predictions, section combi- nations that include the Introduction achieve better results. Combinations of three sections contain Introduction perform better than others, particularly the IRD combination. Despite predicting high novelty scores incorrectly, it still provides a proximate score of 2. Secondly, we conducted an examination of LLM in Figure 7, the pre- diction results indicate that the LLM is proficient in judging papers with novelty scores of 1 and 2. However, for many papers with a novelty score of 0, the LLM often predicts a score of 2. This suggests that the LLM tends to provide favorable results and struggles to identify papers with low novelty. Considering different chapter structures, the predictions for I, IMD, and RD are the closest to the ground truth. Although the number of well-performing section combinations varies, it is evident that the introduction, results, and discussion sections are beneficial for predicting novelty scores. Furthermore, the inclusion of the methods section, supplemented by the introduction and discussion, allows the LLM to better understand the paper’s content and make more accurate novelty score predictions. In summary, through case studies involving PLM and LLM, we can be- lieve that the combination of introduction, results, and discussion sections is more effective for novelty score prediction. 32 Note : The x axis represents the true labels, and the numbers in different colors within each plot indicate the difference between the prediction and true labels. The legend is on the right, with darker colors indicating a greater difference between the predicted labels and the true labels. Figure 7: Case study on LLM (GPT-4o) for novelty score prediction using different paper sections as input. 33 5. Discussion In this section, we will discuss the implication of our study on theoretical | https://arxiv.org/abs/2505.16330v1 |
and practical, and limitation of our study. 5.1. Implication 5.1.1. Theoretical Implication In this study, we collected all the PDF data of papers and their correspond- ing peer review reports from ICLR 2022 and 2023. We then parsed the PDFs and used deep learning models to identify the main text of the papers. Subsequently, we employed deep learning models and LLMs to recognize the structure of the identified main text. Then, we used the novelty score given by the reviewers in the review report as the standard, and fine-tuned the PLMs with different section combinations as inputs. We also verified the performance of PLMs with different section combinations as inputs. Addi- tionally, we conducted small-sample tests to assess the performance of LLMs when provided with different section combinations as content prompts. Based on the results we obtained, both PLMs and LLMs can, to some extent, combine section structure information to predict novelty scores in long-text processing; however, each has its limitations. First, for PLMs, al- though their accuracy and F1scores are relatively high, they appear to be less sensitive to papers with high novelty scores and fail to make accurate predictions in such cases. We believe that the relatively small number of pa- pers with high novelty scores is one reason for this phenomenon. Regarding LLMs, although their performance across various metrics is not ideal, they seem to perform better in predicting papers with high novelty scores. We speculate that this may be because LLMs, which are primarily designed for conversational tasks, tend to exhibit a more favorable and engaging tone. Based on this, we argue that relying solely on PLMs or LLMs for automatic novelty score prediction could lead to polarized results. Since both PLMs and LLMs have their respective strengths, we believe that combining the advantages of both through effective methods may provide a more optimal solution for automatic novelty score prediction. Finally, our results indicate that the optimal section combination for pre- dicting novelty scores is the introduction, results, and discussion. We believe that the introduction section contains the main contributions and innovations proposed by the authors, which are crucial for assessing novelty. The results and discussion sections provide specific explanations of these contributions 34 and further summarize the findings, enabling the model to better understand the task of predicting novelty scores and thus achieve the best performance. While our findings suggest that certain section combinations may enhance classifier performance, these results should not be interpreted as a definitive measure of novelty. Instead, they highlight text-based features that may signal novel contributions, which require further validation through human judgment. Additionally, the method in this study holds theoretical value for fields beyond computer science, particularly for papers in domains where section structure Identification is possible. It is applicable if the content of the paper can be classified into the IMRaD structure, enabling novelty score prediction, or by labeling enough novelty scores for transfer learning. 5.1.2. Practical Implication Firstly, we provide insights for future evaluations of paper novelty. When assessing novelty using the core content of a paper rather than partial | https://arxiv.org/abs/2505.16330v1 |
el- ements (such as abstracts, keywords, etc.), priority should be given to the content found in the introduction, results, and discussion sections, particu- larly focusing on significant sentences and paragraphs. Additionally, if only a portion of the content, such as a single section, is to be considered, our results suggest a prioritization hierarchy. The introduction should be the primary focus, followed by the results and discussion sections. Secondly, in a zero-shot setting, LLMs are unreliable for the task of nov- elty score prediction. This is consistent with the findings of Mike Thelwall (Thelwall, 2024) and Zhou et al. (Zhou et al., 2024), who also concluded that current LLMs are insufficient for fine-grained evaluations. Overall, LLMs consistently generate results that tend to be overly satisfactory, often assign- ing high scores when assessing the novelty of papers. This indicates that the current LLM is not accurate enough to complete formal peer review related work, especially novelty assessment. We believe that fine-tuning these mod- els could potentially enable them to assist humans in the review process. Thirdly, our results can provide guidance for novice reviewers. Young reviewers, who may be unsure how to assess the novelty of a paper in their initial reviews, can begin by focusing on the introduction, results, and dis- cussion sections for a preliminary evaluation. Furthermore, they can lever- age their accumulated knowledge to make more comprehensive judgments, thereby completing the review process with a clear and systematic approach. Finally, our research also can generalizable to other domains. Using ICLR papers and novelty scores as a reference domain, we explored the optimal 35 combination of section structures for predicting novelty scores. To apply this to other domains, it is only necessary to identify the section structures in the target domain and provide the corresponding novelty scores. Transfer learning and other cross-domain learning methods can then be employed to select the optimal structure based on our conclusions. In addition to peer review in ICLR and the field of computer science, peer review in other do- mains also requires reviewers to assess the novelty of a paper. While this assessment may not always take the form of specific scores, it often involves qualitative grading akin to scoring. Therefore, our research can applicable to other fields as well. 5.2. Limitation Our study has several limitations that warrant emphasis. First, We acknowl- edge that novelty assessment is a multifaceted process influenced by factors such as the reviewer’s background, familiarity with cutting-edge research, and interpretation of original contributions. While our method focuses on text analysis, it does not claim to capture the full complexity of human judgment involved in novelty evaluation. The insight proposed in this study should be viewed as part of a broader toolkit for assessing novelty. By identifying patterns within chapter combinations and their potential influence on the perception of novelty, we aim to complement rather than replace the holistic evaluations conducted by reviewers. Additionally, we only tested two LLM for their performance in novelty score prediction; many other available LLMs, such as LLaMA 3, remain to be tested. Furthermore, the prompts used | https://arxiv.org/abs/2505.16330v1 |
in our study represent just one possible form and could benefit from further ex- ploration to design more effective prompts. Although GPT -3.5 or GPT-4o currently possess certain capabilities, they also have limitations. GPT-3.5, for instance, still requires certain functionalities or additional tasks, such as incorporating current scientific databases, to be effectively utilized in these experiments. Our study did not account for the impact of other content in academic papers, such as tables, figures, and charts, which are crucial com- ponents of academic papers. Lastly, the data we used from ICLR is limited to the field of computer science, introducing certain constraints on the gen- eralizability of our findings. Despite these limitations, this study offers a novel perspective on how text-based features can be leveraged to detect potential signals of novelty in academic writing. Our approach lays the groundwork for further explo- ration of the relationship between textual content and perceived innovation 36 in research. 6. Conclusion and future works In this study, we explored three research questions. To achieve this, we con- ducted section structure recognition on academic papers and fine-tuned the PLMs using different section combinations as inputs to verify its effectiveness in novelty score prediction tasks. Additionally, we evaluated the performance of LLMs on the same task. Finally, we discussed which section structures should be prioritized when assessing the novelty of academic papers. From the final results, the performance of the PLMs was generally moderate, with the highest accuracy for the section combination of Introduction, Results, and Discussion reaching only 0.682. Although this accuracy is not high, it still provides some insights. The LLMs performed even worse on the novelty score prediction task but showed decent results in terms of correlation coef- ficients. Our findings suggest that the Introduction, Results, and Discussion sections should be the primary focus when evaluating the novelty of academic papers. In the future, we plan to collect data from a wider array of disciplines and journals to assess novelty, as our current dataset is limited to the field of computer science and conference papers. We will design more effective model architectures to accomplish the task of novelty score prediction, while also incorporating traditional novelty evaluation methods may be achieve better results. More information, such as abstracts and keywords, can be included in the model to examine the effectiveness of these elements. Fur- thermore, we plan to develop strategies to unlock the potential of LLMs, enabling them to perform novelty score prediction more effectively. Explor- ing the integration of deep learning with LLMs is another area we intend to investigate. Moreover, future work could integrate additional dimensions of novelty assessment, such as reviewer expertise, citation patterns, and quali- tative insights, to create a more comprehensive evaluation framework. 7. Acknowledgment This study is supported by the National Natural Science Foundation of China (Grant No. 72074113). 37 References Arts, S., Hou, J., Gomez, J.C., 2021. Natural language pro- cessing to identify the creation and impact of new technolo- gies in patent text: Code, data, and new measures. Re- search Policy 50, 104144. URL: https://www.sciencedirect.com/ science/article/pii/S0048733320302195 , doi: | https://arxiv.org/abs/2505.16330v1 |
https://doi.org/10. 1016/j.respol.2020.104144 . Beltagy, I., Lo, K., Cohan, A., 2019. SciBERT: A pretrained language model for scientific text, in: Inui, K., Jiang, J., Ng, V., Wan, X. (Eds.), Proceed- ings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Lan- guage Processing (EMNLP-IJCNLP), Association for Computational Lin- guistics, Hong Kong, China. pp. 3615–3620. doi: 10.18653/v1/D19-1371 . Beltagy, I., Peters, M.E., Cohan, A., 2020. Longformer: The long- document transformer. URL: https://arxiv.org/abs/2004.05150 , arXiv:2004.05150 . Boudreau, K.J., Guinan, E.C., Lakhani, K.R., Riedl, C., 2016. Looking across and looking beyond the knowledge frontier: Intellectual distance, novelty, and resource allocation in science. Manage. Sci. 62, 2765–2783. URL: https://doi.org/10.1287/mnsc.2015.2285 , doi: 10.1287/mnsc. 2015.2285 . Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert- Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D., 2020. Language models are few-shot learners, in: Proceedings of the 34th International Conference on Neural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA. doi: https://dl. acm.org/doi/abs/10.5555/3495724.3495883 . Caruccio, L., Cirillo, S., Polese, G., Solimando, G., Sundaramurthy, S., Tortora, G., 2024. Can chatgpt provide intelligent diagnoses? a com- parative study between predictive models and chatgpt to define a new 38 medical diagnostic bot. Expert Systems with Applications 235, 121186. doi:https://doi.org/10.1016/j.eswa.2023.121186 . Chen, Z., Zhang, C., Zhang, H., Zhao, Y., Yang, C., Yang, Y., 2024. Explor- ing the relationship between team institutional composition and novelty in academic papers based on fine-grained knowledge entities. The Electronic Library doi: https://doi.org/10.1108/EL-03-2024-0070 . Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., et al., 2023. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24, 1–113. doi: https://dl.acm.org/doi/10.5555/ 3648699.3648939 . Cohan, A., Dernoncourt, F., Kim, D.S., Bui, T., Kim, S., Chang, W., Go- harian, N., 2018a. A discourse-aware attention model for abstractive sum- marization of long documents, in: Walker, M., Ji, H., Stent, A. (Eds.), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo- gies, Volume 2 (Short Papers), Association for Computational Linguistics, New Orleans, Louisiana. pp. 615–621. doi: 10.18653/v1/N18-2097 . Cohan, A., Dernoncourt, F., Kim, D.S., Bui, T., Kim, S., Chang, W., Go- harian, N., 2018b. A discourse-aware attention model for abstractive sum- marization of long documents, in: Walker, M., Ji, H., Stent, A. (Eds.), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo- gies, Volume 2 (Short Papers), Association for Computational Linguistics, New Orleans, Louisiana. pp. 615–621. doi: 10.18653/v1/N18-2097 . Darraz, N., Karabila, I., El-Ansari, A., Alami, N., El Mallahi, M., 2024. Integrated sentiment analysis with bert for enhanced hybrid recommen- dation systems. Expert Systems with Applications , 125533doi: https: //doi.org/10.1016/j.eswa.2024.125533 . Fagerberg, J., 2006. 1 innovation: A guide | https://arxiv.org/abs/2505.16330v1 |
to the literature, in: The Ox- ford Handbook of Innovation. Oxford University Press. doi: 10.1093/ oxfordhb/9780199286805.003.0001 . Foster, J.G., Shi, F., Evans, J., 2021. Surprise! measuring novelty as expec- tation violation . 39 Funk, R.J., Owen-Smith, J., 2017. A dynamic network measure of techno- logical change. Manage. Sci. 63, 791–817. URL: https://doi.org/10. 1287/mnsc.2015.2366 , doi: 10.1287/mnsc.2015.2366 . Gao, Z., Brantley, K., Joachims, T., 2024. Reviewer2: Optimizing review generation through prompt generation. URL: https://arxiv.org/abs/ 2402.10886 ,arXiv:2402.10886 . Guetzkow, J., Lamont, M., Mallard, G., 2004. What is originality in the humanities and the social sciences? American Sociological Review 69, 190–212. doi: 10.1177/000312240406900203 . Guo, M., Ainslie, J., Uthus, D., Ontanon, S., Ni, J., Sung, Y.H., Yang, Y., 2022. LongT5: Efficient text-to-text transformer for long sequences, in: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V. (Eds.), Findings of the Association for Computational Linguistics: NAACL 2022, Association for Computational Linguistics, Seattle, United States. pp. 724–736. doi: 10. 18653/v1/2022.findings-naacl.55 . Hou, J., Wang, D., Li, J., 2022. A new method for measuring the original- ity of academic articles based on knowledge units in semantic networks. Journal of Informetrics 16, 101306. URL: https://www.sciencedirect. com/science/article/pii/S175115772200058X , doi: https://doi.org/ 10.1016/j.joi.2022.101306 . Jeon, D., Lee, J., Ahn, J.M., Lee, C., 2023. Measuring the novelty of scientific publications: A fasttext and local outlier factor approach. Journal of Infor- metrics 17, 101450. doi: https://doi.org/10.1016/j.joi.2023.101450 . Ji, Y., Zhang, Q., Shen, S., Wang, D., Huang, S., 2019. Research on func- tional structure identification of academic text based on deep learning, in: 17TH INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS (ISSI2019), VOL II, INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI. pp. 2712–2713. doi: https://doi.org/10.13266/ j.issn.0252-3116.2019.13.010 . Kocmi, T., Federmann, C., 2023. Large language models are state-of-the-art evaluators of translation quality, in: Nurminen, M., Brenner, J., Koponen, M., Latomaa, S., Mikhailov, M., Schierl, F., Ranasinghe, T., Vanmassen- hove, E., Vidal, S.A., Aranberri, N., Nunziatini, M., Escart´ ın, C.P., For- cada, M., Popovic, M., Scarton, C., Moniz, H. (Eds.), Proceedings of the 40 24th Annual Conference of the European Association for Machine Trans- lation, European Association for Machine Translation, Tampere, Finland. pp. 193–203. URL: https://aclanthology.org/2023.eamt-1.19 . Leibel, C., Bornmann, L., 2024. What do we know about the disruption index in scientometrics? an overview of the literature. Scientometrics 129, 601–639. Li, S., Wang, Q., 2021. A hybrid approach to recognize generic sections in scholarly documents. International Journal on Document Analysis and Recognition (IJDAR) 24, 339–348. doi: https://doi.org/10.1007/ s10032-021-00381-5 . Liang, W., Zhang, Y., Cao, H., Wang, B., Ding, D.Y., Yang, X., Vodrahalli, K., He, S., Smith, D.S., Yin, Y., McFarland, D.A., Zou, J., 2023. Can large language models provide useful feedback on research papers? a large-scale empirical analysis. NEJM AI 0, AIoa2400196. doi: 10.1056/AIoa2400196 , arXiv:https://ai.nejm.org/doi/pdf/10.1056/AIoa2400196 . Liu, M., Xie, Z., Yang, A.J., Yu, C., Xu, J., Ding, Y., Bu, Y., 2024. The prominent and heterogeneous gender disparities in scientific novelty: Evi- dence from biomedical doctoral theses. Information Processing & Manage- ment 61, 103743. doi: https://doi.org/10.1016/j.ipm.2024.103743 . Liu, R., Shah, N.B., 2023. Reviewergpt? an exploratory study on using large language models for paper reviewing. URL: https://arxiv.org/ abs/2306.00622 ,arXiv:2306.00622 . Lo, K., | https://arxiv.org/abs/2505.16330v1 |
Wang, L.L., Neumann, M., Kinney, R., Weld, D., 2020. S2ORC: The semantic scholar open research corpus, in: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J. (Eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Com- putational Linguistics, Online. pp. 4969–4983. doi: 10.18653/v1/2020. acl-main.447 . Lu, W., Huang, Y., Bu, Y., Cheng, Q., 2018. Functional structure identi- fication of scientific documents in computer science. Scientometrics 115, 463–486. doi: https://doi.org/10.1007/s11192-018-2640-y . 41 Luo, Z., Lu, W., He, J., Wang, Y., 2022. Combination of research questions and methods: A new measurement of scientific novelty. Journal of Infor- metrics 16, 101282. doi: https://doi.org/10.1016/j.joi.2022.101282 . Ma, B., Zhang, C., Wang, Y., Deng, S., 2022. Enhancing identifica- tion of structure function of academic articles using contextual infor- mation. Scientometrics 127, 885–925. doi: https://doi.org/10.1007/ s11192-021-04225-1 . Matsumoto, K., Shibayama, S., Kang, B., Igami, M., 2021. Introducing a novelty indicator for scientific research: validating the knowledge-based combinatorial approach. Scientometrics 126, 6891–6915. doi: https:// doi.org/10.1007/s11192-021-04049-z . Meta, A., 2024. Introducing meta llama 3: The most capable openly available llm to date. Meta AI URL: https://ai.meta.com/blog/meta-llama-3/ . Nair, P.R., Nair, V.D., 2014. Scientific writing and communication in agri- culture and natural resources. Springer. doi: https://doi.org/10.1007/ 978-3-319-03101-9 . Nelson, R.R., 1985. An evolutionary theory of economic change. harvard university press. doi: https://doi.org/10.2307/2232409 . OpenAI, 2024. Gpt-4 technical report. URL: https://arxiv.org/abs/ 2303.08774 ,arXiv:2303.08774 . Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R., 2022. Training language models to follow instructions with human feedback, in: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc.. pp. 27730–27744. URL: https://proceedings.neurips.cc/paper_files/paper/2022/file/ b1efde53be364a73914f58805a001731-Paper-Conference.pdf . Patsakis, C., Casino, F., Lykousas, N., 2024. Assessing llms in malicious code deobfuscation of real-world malware campaigns. Expert Systems with Applications 256, 124912. doi: https://doi.org/10.1016/j.eswa.2024. 124912 . 42 Qin, C., Zhang, C., 2023. Which structure of academic articles do referees pay more attention to?: perspective of peer review and full-text of aca- demic articles. Aslib Journal of Information Management 75, 884–916. doi:https://doi.org/10.1108/AJIM-05-2022-0244 . Robertson, Z., 2023. Gpt4 is slightly helpful for peer-review assis- tance: A pilot study. URL: https://arxiv.org/abs/2307.05492 , arXiv:2307.05492 . Rogers, M., 1998. The definition and measurement of innovation. WorkingPa- per 10/98. Melbourne Institute of Applied Economic and Social Research. Runco, M.A., Jaeger, G.J., 2012. The standard definition of creativity. Cre- ativity Research Journal 24, 92–96. doi: 10.1080/10400419.2012.650092 . Schumpeter, J., 2006. Business Cycles: A Theoretical, Historical, and Sta- tistical Analysis of the Capitalist Process. Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process, Martino Pub. doi:https://doi.org/10.1086/ahr/46.1.96 . Shafee, S., Bessani, A., Ferreira, P.M., 2024. Evaluation of llm-based chatbots for osint-based cyber threat awareness. Expert Systems with Applications , 125509doi: https://doi.org/10.1016/j.eswa.2024.125509 . Shen, C., Cheng, L., Nguyen, X.P., You, Y., Bing, L., 2023. Large language models are not yet human-level evaluators for abstractive summarization, in: Bouamor, H., Pino, J., Bali, | https://arxiv.org/abs/2505.16330v1 |
K. (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Compu- tational Linguistics, Singapore. pp. 4215–4233. doi: 10.18653/v1/2023. findings-emnlp.278 . Shibayama, S., Wang, J., 2020. Measuring originality in science. Scientomet- rics 122, 409–427. Shibayama, S., Yin, D., Matsumoto, K., 2021. Measuring novelty in science with word embedding. PloS one 16, e0254034. doi: https://doi.org/10. 1371/journal.pone.0254034 . Sollaci, L.B., Pereira, M.G., 2004. The introduction, methods, results, and discussion (imrad) structure: a fifty-year survey. Journal of the medical library association 92, 364. 43 Tahamtan, I., Bornmann, L., 2018. Creativity in science and the link to cited references: Is the creative potential of papers reflected in their cited references? Journal of Informetrics 12, 906–930. doi: https://doi.org/ 10.1016/j.joi.2018.07.005 . Thelwall, M., 2024. Can chatgpt evaluate research quality? Journal of Data and Information Science 9, 1–21. doi: doi:10.2478/jdis-2024-0013 . Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozi` ere, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample, G., 2023a. Llama: Open and efficient foun- dation language models. URL: https://arxiv.org/abs/2302.13971 , arXiv:2302.13971 . Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Ferrer, C.C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P.S., Lachaux, M.A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E.M., Subramanian, R., Tan, X.E., Tang, B., Taylor, R., Williams, A., Kuan, J.X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., Scialom, T., 2023b. Llama 2: Open foundation and fine-tuned chat models. URL: https://arxiv.org/abs/2307.09288 ,arXiv:2307.09288 . Uzzi, B., Mukherjee, S., Stringer, M., Jones, B., 2013. Atypical combinations and scientific impact. Sci- ence 342, 468–472. doi: 10.1126/science.1240474 , arXiv:https://www.science.org/doi/pdf/10.1126/science.1240474 . Wang, H., 2024. A content-based novelty measure for scholarly publications: A proof of concept, in: Sserwanga, I., Joho, H., Ma, J., Hansen, P., Wu, D., Koizumi, M., Gilliland, A.J. (Eds.), Wisdom, Well-Being, Win-Win, Springer Nature Switzerland, Cham. pp. 409–420. Wang, J., Veugelers, R., Stephan, P., 2017. Bias against novelty in science: 44 A cautionary tale for users of bibliometric indicators. Research Policy 46, 1416–1436. doi: https://doi.org/10.1016/j.respol.2017.06.006 . Wang, Z., Zhang, H., Chen, J., Chen, H., 2024. An effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model. Journal of Informetrics 18, 101587. doi: https: //doi.org/10.1016/j.joi.2024.101587 . Wu, W., Xi, H., Zhang, C., 2024. Are the confidence scores of review- ers consistent with the review content? evidence from top conference proceedings in ai. Scientometrics , 1–27doi: https://doi.org/10.1007/ s11192-024-05070-8 . Yin, D., Wu, Z., Yokota, K., Matsumoto, K., Shibayama, S., 2023. Identify novel elements of knowledge with word embedding. Plos one 18, e0284567. doi:https://doi.org/10.1371/journal.pone.0284567 . Zaheer, M., | https://arxiv.org/abs/2505.16330v1 |
Guruganesh, G., Dubey, K.A., Ainslie, J., Alberti, C., On- tanon, S., Pham, P., Ravula, A., Wang, Q., Yang, L., Ahmed, A., 2020. Big bird: Transformers for longer sequences, in: Larochelle, H., Ran- zato, M., Hadsell, R., Balcan, M., Lin, H. (Eds.), Advances in Neu- ral Information Processing Systems, Curran Associates, Inc.. pp. 17283– 17297. URL: https://proceedings.neurips.cc/paper_files/paper/ 2020/file/c8512d142a2d849725f31a9a7a361ab9-Paper.pdf . Zhou, R., Chen, L., Yu, K., 2024. Is LLM a reliable reviewer? a compre- hensive evaluation of LLM on automatic paper reviewing tasks, in: Cal- zolari, N., Kan, M.Y., Hoste, V., Lenci, A., Sakti, S., Xue, N. (Eds.), Proceedings of the 2024 Joint International Conference on Computa- tional Linguistics, Language Resources and Evaluation (LREC-COLING 2024), ELRA and ICCL, Torino, Italia. pp. 9340–9351. URL: https: //aclanthology.org/2024.lrec-main.816 . Zhou, S., Li, X., 2020. Feature engineering vs. deep learning for pa- per section identification: Toward applications in chinese medical liter- ature. Information Processing & Management 57, 102206. doi: https: //doi.org/10.1016/j.ipm.2020.102206 . Zhu, C., Yi, B., Luo, L., 2025. Aspect-based sentiment analysis via bidirec- tional variant spiking neural p systems. Expert Systems with Applications 259, 125295. doi: https://doi.org/10.1016/j.eswa.2024.125295 . 45 | https://arxiv.org/abs/2505.16330v1 |
Embodied Agents Meet Personalization: Exploring Memory Utilization for Personalized Assistance Taeyoon Kwon∗1Dongwook Choi∗1Sunghwan Kim1Hyojun Kim1 Seungjun Moon1Beong-woo Kwak1Kuan-Hao Huang2Jinyoung Yeo1 1Yonsei University2Texas A&M University Abstract Embodied agents empowered by large language models (LLMs) have shown strong performance in household object rearrangement tasks. However, these tasks pri- marily focus on single-turn interactions with simplified instructions, which do not truly reflect the challenges of providing meaningful assistance to users. To provide personalized assistance, embodied agents must understand the unique semantics that users assign to the physical world ( e.g., favorite cup, breakfast routine) by leveraging prior interaction history to interpret dynamic, real-world instructions. Yet, the effectiveness of embodied agents in utilizing memory for personalized as- sistance remains largely underexplored. To address this gap, we present MEMENTO , a personalized embodied agent evaluation framework designed to comprehensively assess memory utilization capabilities to provide personalized assistance. Our framework consists of a two-stage memory evaluation process design that enables quantifying the impact of memory utilization on task performance. This process enables the evaluation of agents’ understanding of personalized knowledge in object rearrangement tasks by focusing on its role in goal interpretation: (1) the ability to identify target objects based on personal meaning ( object semantics ), and (2) the ability to infer object–location configurations from consistent user patterns, such as routines ( user patterns ). Our experiments across various LLMs reveal significant limitations in memory utilization, with even frontier models like GPT-4o experiencing a 30.5% performance drop when required to reference multiple memories, particularly in tasks involving user patterns. These findings, along with our detailed analyses and case studies, provide valuable insights for future research in developing more effective personalized embodied agents. Project website: https://connoriginal.github.io/MEMENTO 1 Introduction Embodied agents empowered by large language models (LLMs) have recently demonstrated remark- able success in executing object rearrangement tasks in household environments [ 18,49,44,7,28]. As the primary objective of embodied agents is to provide assistance to users while interacting with the physical world, leveraging LLMs’ natural language understanding and reasoning capabilities lead embodied agents to effectively interpret user instructions into sets of target object-location pairs that the agent should rearrange to successfully accomplish the task. But do such tasks truly reflect the challenges in providing meaningful assistance to the users? As illustrated in Figure 1, conventional embodied tasks predominantly focus on single-turn interactions with static and simplified instructions that the agents could simply follow without implicit reasoning ∗Equal contribution Preprint. Under review.arXiv:2505.16348v1 [cs.CL] 22 May 2025 Pr e vious Embodied T ask sP ersonaliz ed Assis tance T ask s Bring m y f a v orite cup and pr epar e m y ligh t br eakf as t se tup My F a v orite Cup My Br eakf as t R outine “Bring the cup and place it on the righ t side o f the table ”Wha t w as the f a v orite cup ? Wha t w as the br eakf as t r outine? Firs t, pick the cup . Ne xt, ... Is simply f ollo wing ins tructions enough f | https://arxiv.org/abs/2505.16348v1 |
or ?personaliz ed embodied agen tFigure 1: Comparison between traditional embodied tasks and personalized assistance tasks. Previous works focus on strictly following simple instructions, while personalized assistance agents must know user-specific knowledge, which require grounding in past interactions. This highlights the challenge of going beyond instruction-following toward context-aware personalized embodied agent. to comprehend user intentions [ 2,17,44,50]. However, for personalized embodied agents, it is important to understand personalized knowledge that users assign unique semantics to the physical world ( e.g., favorite cup, breakfast routine) to interpret dynamic instructions. To provide personalized assistance, agents must effectively leverage memories that retain personalized knowledge from previous interactions—especially episodic memory, which enables the recall of specific events grounded in time and space [ 20]. Without such memory utilization, embodied agents require users to repeatedly provide detailed instructions, which may hinder user engagement and prevent natural human-agent interaction. Despite its importance, the effectiveness of embodied agents in utilizing episodic memory containing personalized knowledge remains largely underexplored. In this work, we present MEMENTO , a personalized embodied agent evaluation framework designed for comprehensive assessment of memory utilization for providing personalized assistance. To enable a thorough analysis of memory utilization, we divide the memory evaluation process into two stages. In the Memory Acquisition Stage , agents perform tasks with instructions containing personalized knowledge while accumulating the interaction history. Subsequently, the Memory Utilization Stage challenges agents to complete the same tasks as in the memory acquisition stage but with modified instructions that is difficult to succeed without referencing the previously acquired personalized knowledge. This design allows us to systematically quantify the impact of memory utilization on task performance. Building upon this evaluation process, we aim to analyze agents’ ability to understand personalized knowledge in object rearrangement by focusing on its role in goal interpretation: (1) the ability to identify target objects based on personal meaning ( object semantics ), and (2) the ability to infer object–location configurations from consistent user patterns, such as routines ( user patterns ). Based on MEMENTO , we evaluate embodied agents powered by a range of LLMs with varying capabilities, covering both open-source and proprietary models. Our findings reveal that even frontier LLMs struggle to utilize episodic memory with personalized knowledge, with GPT-4o exhibiting a 30.5% performance drop when required to reference multiple memories. Further analysis shows that this performance degradation is particularly pronounced in tasks involving user patterns, and that the agents are highly susceptible to irrelevant memories acting as distractors. We also conduct error and success case analyses to understand how embodied agents reference memories during task execution, providing valuable insights to guide future research in developing personalized embodied agents. To summarize, our contributions are as follows: •We propose MEMENTO , a novel personalized embodied agent evaluation framework designed to assess agents’ ability to utilize episodic memory for providing personalized assistance in object rearrangement tasks. •To quantify the impact of memory and analyze understanding of personalized knowledge independently of reasoning capabilities, we decompose memory usage into two stages: Memory Acquisition and Memory Utilization. 2 •Through extensive experiments and analysis, we identify key limitations | https://arxiv.org/abs/2505.16348v1 |
of current LLM- powered embodied agents in leveraging personalized knowledge from memory, and offer insights to guide future research on personalized embodied agents. 2 Related Work LLM-powered embodied agents. LLMs have significantly advanced embodied agents’ reasoning and planning capabilities in recent years. Researchers have explored LLMs for interpreting user goals [ 2], high-level task planning [ 17], and integrating LLMs into comprehensive embodied agent frameworks [ 18,29,34,44,19]. Other research directions have focused on generating executable code for embodied tasks directly from language instructions [ 30,47,43], while various benchmarks have been developed to evaluate embodied reasoning abilities [ 26,9,7,28]. Collectively, these studies highlight the promise of LLM-powered agents in bridging language understanding and physical interaction. Memory systems for embodied agents. Previous studies on memory systems for embodied agents have primarily focused on semantic memory ( e.g., scene graph, semantic map), which store and provide state information about the current environment [ 39,23,16,51,48], or on procedural memory ( e.g., skill library) that stores action primitives, focusing on how to perform tasks to enhance the efficiency on generating the low-level action code [ 47,42,59]. Another important category is episodic memory , which captures specific past interactions and experiences with users. However, prior uses of episodic memory have mostly treated it as passive task buffers [ 2,43] or histories for in-context [ 18,31,44,8], without explicitly evaluating its role in personalized task grounding or systematic memory utilization. Personalization for embodied agents. The importance of personalization in robotics has long been recognized [ 13,25,10], particularly in the context of human-robot interaction where robots adapt their interactive behaviors to align with individual users. Recent works have focused on reflecting individual user’s preferences during embodied agents’ task execution, such as spatial arrangement [ 22,49], table settings [ 37], or personalized object navigations [ 11,4]. Recently, Xu et al. [52] aim to infer user preferences from a few demonstrations and adapt planning behavior accordingly. However, these approaches primarily focus on implicit preference adaptation or short- term reactive behaviors, without modeling user-specific knowledge in a structured manner. 3 Preliminaries We formulate the object rearrangement task for LLM-powered embodied agents as a Partially Observable Markov Decision Process (POMDP) defined by the tuple (S, A, T, R, Ω, O, γ), where S denotes the set of environment states, Ais the set of actions, Tis the transition function, Ris the reward function, Ωis the observation space, Ois the observation function, and γthe discount factor. At each timestep t, the environment is in a state st∈S, and the agent receives a partial observation wt∈Ωin text modality describing visible objects near its current position. At the beginning of each episode, the agent is given a natural language instruction I(e.g., Place the mug on the table and the book on the shelf). The instruction is grounded into a symbolic representation of the ground-truth goalg, denoted g={(oi, li)}k i=1, where each pair (oi, li)are the target object oi(e.g., mug) that should be placed at the specified location li(e.g., on the table). In order to execute the instruction, the agent must internally derive the goal representation ϕ(I)−→g, where ϕdenotes the | https://arxiv.org/abs/2505.16348v1 |
instruction grounding function, to guide the policy’s decision-making. Given an instruction I, where the policy πis implemented by an LLM, the agent generates actions at timestep tbased on the trajectory of observations and actions: π(I, τt)→at, τ t= (w1, a1, w2, a2, . . . , w t−1, at−1, wt) (1) The goal is to produce a sequence of actions a1:t= (a1, a2, . . . , a t)such that the resulting state st satisfies the agent’s goal g(st) = 1 . 3 En vir onmen tMemory A cquisition StageMemory Utiliza tion Stage M EMENT O : P ersonaliz ed Embodied A gen t E v alua tion F r ame w ork Mo v e cup to the living r oom table, and mo v e bo wl... T he cup is blue. T ha t’ s m y f a v orite cup!Mo v e bo wl ... and then, put apple on the bo wl. It’ s !m y ligh t br eakf as t se ttingU ser P a ttern: R outineObject Seman tics: Pr e f er ence T ask ins truction + A dditional in f o + P ersonaliz ed kno wledgeT ask ins truction + P ersonaliz ed kno wledge E p iso d ic MemoryA ccumula te the hi s tor y o f in ter actionU tili z e epi s odic memor y f or per s onali z ed ta skE pi s ode #1 Mo v e to the living r oom table ...m y f a v orite cupObject Seman tics: Pr e f er ence P ersonaliz ed Ins tructionS ingle - memor y T a sk Mo v e to the living r oom table ... pr epar e !m y f a v orite cupand then,m y ligh t br eakf as t se ttingObject Seman tics: Pr e f er ence Sequen tial P ersonaliz ed Ins tructionJ oin t - memor y T a skE pi s ode #2 E pi s ode #1E pi s ode #2U ser P a ttern: R outine T as k I ns tructionMo v e cup to the living r oom table ... T ha t’ s m y f a v orite cup! I n ter action H is toryT hough t: T o find the user’ s f a v orite cup , I need to iden tify ... A ct: DescribeObject[ cup_1] R esults: A blue cup .T as k I ns truction... and then, put apple on the bo wl. It’ s m y ligh t br eakf ast se tting!I n ter action H is toryT hough t: T o se t up the user’ s ligh t br eakf ast se tting, I should mo v e... A ct: Pick[bo wl_1] R esults: Success... A ct: D escribe O b j ect [ cup _1] R esults: A blue cup . T hough t: Based on the pr e vious e x ample , the user’ s cup is blue | https://arxiv.org/abs/2505.16348v1 |
. A ct: Pick [ cup _1] ...... A ct: Pick[bo wl_1] R esults: Success .. . T hough t: N e x t, I will pr epar e the user’ s ... Based on the pr e vious e x ample , .. . A ct: F ind O b j ect [ bo wl ] ...E p iso d e #1 ( Memory )E p iso d e #2 ( Memory )E p iso d e #1 ( Memory ) Figure 2: Overview of M EMENTO . 4 M EMENTO In this section, we introduce MEMENTO , a personalized embodied agent evaluation framework designed to assess how well embodied agents leverage episodic memory containing personalized knowledge to provide personalized assistance. We begin by describing how we design the memory evaluation process (§ 4.1), categorize personalized knowledge (§ 4.2), the data construction process (§ 4.3), and the validation of our evaluation framework (§ 4.4). 4.1 Memory Evaluation Process Design Two-stage evaluation process. The major challenge of evaluating embodied agents’ memory utilization capability is to quantify the memory effect on the overall task performance. To address this limitation, as shown in Figure 2, we divide the evaluation process into two stages. •Memory Acquisition Stage: Agents perform tasks with conventional object rearrangement task instructions containing personalized knowledge while accumulating the interaction history ( i.e., episodic memory). The goal of this stage is to provide a reference performance baseline. •Memory Utilization Stage: Agents execute the same tasks as in the memory acquisition stage but with modified instructions that require agents to recall and apply the previously acquired personalized knowledge to succeed. The goal of this stage is to evaluate how well agents can utilize memory by comparing the performance drop relative to the acquisition stage. Given the base object rearrangement task episode defined as the tuple ϵ= (S, I, g ), the key concept of our evaluation process is to share the scene Sand goal representation g, while varying the instruction Iacross the two stages to isolate instruction interpretation capability as the primary factor influencing performance differences. Formally, in the memory acquisition stage , each episode is defined as ϵacq= (S, I acq, g), where the instruction Iacqcontains sufficient information to infer the goal g, denoted as: ϕ(Iacq)−→g (2) During this stage, we also store the episodic memory hacq: hacq= (Iacq, τk)∈Hacq, τ k= (w1, a1, w2, a2, . . . , w k−1, ak−1, wk) (3) In the subsequent memory utilization stage , each episode is defined as ϵutil= (S, I util, g), where the instruction Iutilis intentionally underspecified and requires the agent to recall the corresponding episodic memory hacqto correctly interpret the goal g, formally: ϕ(Iutil, hacq)−→g (4) Through this design concept, we are able to quantify the agent’s ability to utilize memory by comparing performance between the two stages. 4 Memory utilization stage task design. Within our two-stage evaluation process design, we can assess embodied agents’ ability to utilize personalized knowledge from a single episodic memory. However, this approach alone fails to capture real-world complexity and lacks analytical diversity for comprehensive | https://arxiv.org/abs/2505.16348v1 |
assessment. Therefore, to evaluate different levels of memory complexity, we divide our assessment into (1) Single-memory task , which require utilizing information from one episodic memory, and (2) Joint-memory task , which necessitate synthesizing information from two distinct episodic memories to successfully complete the episode. Building on our evaluation process concept, we form the joint-memory task by concatenating two episodes, which can be formulated as: ϵjoint util= (S, Ijoint util,[gi;gj]) (5) where i,jdenotes the corresponding reference episodes from the memory acquisition stage. 4.2 Personalized Knowledge Categorization Building upon our memory evaluation process, we aim to analyze embodied agents’ ability to understand personalized knowledge in object rearrangement tasks by focusing on its role in goal interpretation, ϕ(I)→g={(oi, li)}k i=1, where each oiandlidenote the target object and location, respectively. To facilitate this analysis, we categorize personalized knowledge into two types, each comprising subcategories2that reflect how users naturally express preferences and routines in real- world interactions. Each type is designed to isolate a distinct reasoning challenge that the agent must resolve by utilizing episodic memory during the memory utilization stage. •Object semantics: Individual objects that the user assigns personal meaning, encompassing subcategories such as ownership ( e.g.,my cup ), preference ( e.g.,my favorite running gear ), past history ( e.g.,a graduation gift from my grandma ), or grouped references ( e.g.,my childhood toy collections ). This category tests whether the agent can identify the target object oiby recalling its personal meaning from prior interactions. •User patterns: Sequences of actions that the user consistently performs, including personal rou- tines ( e.g.,my remote work setup ) and arrangement preferences ( e.g.,my cozy dinner atmosphere ) across recurring contexts. This category evaluates the agent’s ability to reconstruct the complete goalgby leveraging previously observed behavioral patterns across multiple objects and locations. 4.3 Dataset Construction Process We constructed the dataset for MEMENTO through a four-step process using the Habitat 3.0 simu- lator [ 38] as the environment, with a simulated Spot robot as the agent [ 5,58]. Our custom dataset spans 12 scenes, comprising a total of 438 episodes distributed across stages. Notably, the mem- ory acquisition stage and the single-memory task in the utilization stage have the same number of episodes, whereas the joint-memory task contains fewer episodes. The detailed dataset statistics and the explanation of the construct process is described in Appendix C.3, and Appendix C.4. Step 1: Object rearrangement task collection. We use the test set of the PartNR [ 7] as our foundation object rearrangement task data. Unlike simple pick-and-place tasks, PartNR episodes require completing multiple object–location pairs within a single instruction, which aligns with our memory evaluation process design. Step 2: Scene augmentation with distractor objects. In the original scenes, there was an issue where no objects of the same type as the target object were present. As a result, the agent could identify the target object without needing to understand personalized knowledge. To address this, we augmented the scenes by placing distractor objects of the same type near the target object. For example, if the target object is a “blue cup” on the table, | https://arxiv.org/abs/2505.16348v1 |
we place a “red cup” next to it as a distractor. Step 3: Task instruction generation. We first generate personalized knowledge contextually tailored to the original task instruction using GPT-4o. With the generated personalized knowledge, we applied to both stage instruction curation. As illustrated in Figure 2, the memory acquisition stage instruction Iacqis constructed by concatenating the base instruction, object visual captions (only for 2A detailed explanation of the sub-categories of personalized knowledge can be found in Appendix C.1. 5 episodes of the object semantics type), and the generated personalized knowledge. This ensures that the goal can be inferred directly from the instruction. For the memory utilization stage , we prompt GPT-4o to generate a personalized instruction that implicitly reflects the personalized knowledge, based on the same base instruction. For joint-memory tasks, we concatenate two such personalized instructions sequentially. Figure 3: The performance results with- out using episodic memory. Original indicates the conventional object rear- rangement task episodes.Step 4: Quality control. To ensure data quality, we first heuristically filtered episodes containing similar memo- ries referencing identical objects within scenes, preventing interference between similar episodic memories. Subse- quently, we manually reviewed episodes from the mem- ory acquisition stage where GPT-4o failed to successfully complete the task, where we filtered out episodes that con- tained unnatural instructions or cases where the generated instructions did not match the intended goal representation, ensuring the quality of our evaluation data. 4.4 Validation of M EMENTO To validate that MEMENTO effectively assesses embodied agents’ memory utilization capability, we compare perfor- mance across stages in a setup without memory retrieval. As shown in Figure 3, compared to the results of the orig- inal object rearrangement task and the memory acquisition stage, embodied agents struggle to complete tasks in the memory utilization stage. Since the underlying episode re- mains the same across all tasks, this result confirms that the instruction Iutilin the memory utilization stage is difficult to interpret without access to previous interaction histories. Notably, we observed a particular behavior of the embodied agents: when there are two objects of the same type, agents that do not understand which one is the actual target tend to randomly select one and proceed with the task. This explains the reason why performance in the single-memory task is higher than that of the joint-memory task. 5 Evaluating Personalized Knowledge Utilization in Episodic Memory 5.1 Experimental Setup Evaluation metrics. Following Chang et al. [7], we use two main metrics: Percent Complete (PC) for the proportion of goal completion, and Success Rate (SR) for full task completion. We also report Sim Steps , which show the number of simulation steps required for agents to complete the task, and Planning Cycles , which indicate the number of LLM inference calls made during task execution. To evaluate memory utilization, we also report performance drops between acquisition and utilization stages as ∆PCand∆SR. Note that for joint-memory tasks, these differences are computed relative to the average performance of the corresponding acquisition-stage episodes. Implementations. Following prior work [ 45,38,7], we implement a LLM-powered embodied agent architecture, where the LLM | https://arxiv.org/abs/2505.16348v1 |
functions as a high-level policy planner that selects appropriate skills from a predefined skill library. We use ReAct [ 56] prompt format for LLMs to take actions. Additionally, we implement a top-5 memory retrieval setup3for the memory utilization stage, ensuring the corresponding memory is included in the retrieved results by randomly replacing one memory if the correct one is not initially retrieved. Full implementation details are in AppendixB.2. Models. We evaluate embodied agents powered by a range of LLMs to compare memory utilization capabilities across model families and sizes, including proprietary models (GPT-4o [ 21], Claude-3.5- Sonnet [3]) and open-source models (Llama-3.1-70b/8b [15], Qwen-2.5-72b/7b [54]). 3We use an embedding-based retrieval method with the all-mpnet-base-v2 Sentence Transformer [40]. 6 Table 1: Model performance across memory acquisition and utilization stage in M EMENTO . Model StageTask Type (Memory)Planning Cycles ↓Sim Steps ↓Percent Complete ↑∆PCSuccess Rate ↑∆SR GPT-4oAcquisition - 16.5 2156.1 96.3 - 95.0 - UtilizationSingle 16.1 2450.8 88.0 -8.3 85.1 -9.9 Joint 28.9 3480.7 86.7 -10.5 63.9 -30.5 Claude-3.5-SonnetAcquisition - 16.0 2104.1 96.2 - 94.0 - UtilizationSingle 15.3 2258.8 69.3 -26.9 63.7 -30.3 Joint 27.8 3198.8 64.6 -30.1 33.3 -57.0 Qwen-2.5-72bAcquisition - 17.5 2281.9 93.5 - 91.0 - UtilizationSingle 17.5 2691.2 72.6 -20.9 67.2 -23.8 Joint 31.3 4027.1 68.9 -27.9 36.1 -58.3 Llama-3.1-70bAcquisition - 17.7 2162.1 92.9 - 90.0 - UtilizationSingle 19.0 2566.6 72.2 -20.7 66.7 -23.3 Joint 31.4 3425.2 51.3 -44.9 8.3 -83.4 Llama-3.1-8bAcquisition - 19.3 2377.0 78.1 - 68.5 - UtilizationSingle 19.0 3131.7 48.1 -30.0 35.0 -33.5 Joint 27.4 3478.2 35.3 -45.5 8.3 -59.8 Qwen-2.5-7bAcquisition - 21.7 2476.8 64.1 - 53.2 - UtilizationSingle 21.8 3271.0 39.1 -25.0 27.4 -25.8 Joint 26.9 4149.0 33.7 -34.2 5.6 -52.7 5.2 Main Results LLM-powered embodied agents struggle with understanding personalized knowledge. As shown in Table 1, while GPT-4o maintains a relatively high success rate in the single-memory task, all models show a success rate drop over 20% compared to the memory acquisition stage. In particular, for joint-memory tasks even GPT-4o exhibits a 30.5% drop in success rate, highlighting the increased difficulty of these settings. This substantial performance decline demonstrates that even frontier models struggle to accurately reference personalized knowledge from episodic memory, and often fail to consistently apply it across multiple steps in long-horizon task planning. Figure 4: The results of personalized knowl- edge type based analysis (single-memory).LLM-powered embodied agents exhibit increased exploration behavior on joint-memory tasks. Joint-memory task results reveal that LLMs (even GPT-4o) find it difficult to recall personalized knowl- edge from different memory sources. The number of planning cycles and simulation steps significantly increases compared to other tasks, this suggests that the embodied agent fails to correctly interpret the in- struction, leading to excessive exploration during task execution. Also the performance gap between per- cent complete and success rate is larger than in the single-memory task, which indicates that the agent frequently misses part of the necessary information for successful task completion. 5.3 In-depth Analysis To better understand the challenges of providing personalized assistance, we examine two key aspects: the root causes of their limitations and how the top- kmemory setting affects memory | https://arxiv.org/abs/2505.16348v1 |
utilization. 5.3.1 Personalized Knowledge Type-based Analysis We analyze the performance gap between the memory acquisition stage and the single-memory task from the memory utilization stage by comparing success rates across different types of personalized knowledge. Analysis results for the joint-memory task are provided in Appendix D.2. 7 Figure 5: Success rate comparison across models as top-k value increases. Dashed lines represent memory acquisition stage baselines for each model. Task: Can y ou arr ange m y morning essen tials in the bedr oom ? Thought: T o arr ange ..., I first need to iden tify wha t these essen tials ar e ... such as a , , or .cup k e ttle pla teR eason: Omitted k e y chain.D . 1 Commonsense R easoningXTask: Could y ou se tup the candle f or a co z y e v ening? Thought: Based on pr e vious e x amples, ... is a .placing the candle on a couch common se tup f or a co z y a tmospher eR eason: Skipped middle s tep . ( candle → table → couch )D .2 Inaccur a te R ecallXOTask: Could y ou se t up m y plan ts in their usual spo t? Thought: T o de termine their usual spo t, , ...I will r e f er to pr e vious e x amplesC .2 Dir ect R e f er enceOTask: Can y ou arr ange m y a fternoon r e fr eshmen t se tup ? Thought: ... I will start b y e xploring the kitchen, as it is a ... ... Thought: ... a jug and a cup , ... .common place lik ely part o f the a fternoon r e fr eshmen t se tupC . 1 Commonsense R easoningU ser P a tternR eason: F ai l ed to r eca ll the desc r i p tion o f the g i f t b ook.Task: ... bring gift b oo k fr om m y b es t friend ? Thought: ...Neither book_1 nor book_8 ma tchesXB . 3 Inaccur a te R ecallR eason: Gu essed w r on g shoe ( shoe _0 ) as a ta r g e t.Task: Could y ou bring m y f a v orite runnin g g ear ? Thought: ... pick up the f a v orite running gear , .which is lik ely shoe_0XB .2 H allucina tionXB . 1 M isse d P ersonali z a tion CueR eason: M isin te rpr e ted u se r ’ s in ten t to u ti l i z e p e r sona l i z ed kno w l ed g e. Task : Can y ou bring the gifted to y fr om m y b es t friend to the ta b le? .. . ( Be f or e observing a to y ) Thought : S ince , ...the to y is a gift fr om m y | https://arxiv.org/abs/2505.16348v1 |
bes t friendA . 1 Obj ect R e f er enceOTask : Could y ou place the b r ead fr om m y f a v orite b a k ery ... a Thought : I ha v e f ou n d mult i ple in sta n ces o f br ead, ... I n eed to i de n t i fy the spec i fic br ead .. . Action : Thought : ‘B r ead_0’ .DescribeObject T ool[br ead_0 ] ma tches the descrip tionObj e c t S e m an t ic s Figure 6: Examples of success and error cases in memory utilization stage. Top: success and failure cases of object semantics; Bottom: success and failure cases of user patterns. LLMs can recall objects but struggle to comprehend action sequences. As shown in Figure 4, all models exhibit substantially larger performance drops for tasks requiring understanding user patterns compared to object semantics. Notably, the performance drop in object semantics is minimal, indicating that LLMs are relatively effective at directly recalling relevant memories to identify target objects. In contrast, tasks involving user patterns pose a significantly greater challenge, as they require integrating and reasoning over sequences of events. 5.3.2 Memory Top-K Analysis We analyze how varying the number of retrieved memories ( k) impacts the agent’s ability to utilize personalized knowledge from episodic memory. Irrelevant memories act as a distraction to agents. In Figure 5, we observe that as kincreases, all models exhibit consistent performance degradation across both task types, highlighting the difficulty of identifying the exact information from a growing set of retrieved memories. Consistent with previous findings, the degradation is especially pronounced in tasks requiring user patterns understanding, suggesting that such tasks are particularly vulnerable to noise when recalling and executing multi-step procedures grounded in implicit, personalized knowledge. 5.4 Empirical Analysis of Success and Error Cases To better understand how LLM-powered embodied agents utilize personalized knowledge derived from episodic memory during task planning, we conduct qualitative case studies of both successful and failed episodes. Figure 6 presents the taxonomy of case types along with illustrative examples. Detailed explanations for each case type are provided in Appendix D.4. Below, we highlight a set of representative cases that offer key insights into the embodied agents’ behavior. 8 •Agent misses personalization cues (B.1): The agent fails to recognize user-specific references and treats them as generic or proper nouns, stemming from incorrect interpretations of user intent. •Use of commonsense knowledge over personalized knowledge (C.1 & D.1): Even when relevant episodic memory is available, the agent often relies on general commonsense knowledge to infer user routines, rather than using the personalized knowledge. This tendency appears in both successful and failed episodes. 6 Discussion 6.1 The Impact of Trajectory in Episodic Memory Table 2: Analysis of how memory type af- fects agent performance: (a) complete ac- tion action trajectories containing user in- structions, (b) summary of (a), and (c) user instructions only. Model Memory PC (%) SR (%) GPT-4o(a) 90.0 83.3 (b) 88.0 83.3 (c) 62.4 50.0 | https://arxiv.org/abs/2505.16348v1 |
Qwen-2.5-72b(a) 77.2 66.7 (b) 77.4 70.0 (c) 51.3 40.0 Llama-3.1-8b(a) 72.8 63.3 (b) 49.4 43.3 (c) 40.0 30.0 Qwen-2.5-7b(a) 50.1 43.3 (b) 43.9 36.7 (c) 35.6 23.3MEMENTO ’s design incorporates complete action- observation trajectories in episodic memory, raising the question of whether agents should reference these detailed trajectories rather than relying solely on user instructions. To investigate this, we conduct an addi- tional comparative experiment to evaluate if provid- ing full action trajectories offers significant advantages over simply providing instructions that state personal- ized knowledge. For the experiment setup, we evaluate model performance across three different cases of mem- ory provided to the agent as shown in Table 2. The results reveal that while larger models (GPT-4o, Qwen- 2.5-72b) perform well with only high-level plans (b), smaller models (Llama-3.1-8b, Qwen-2.5-7b) require full procedural details from completed trajectories (a) to succeed. Most notably, all models show substantial per- formance drops when given only user instructions (c), suggesting that action trajectories contain essential pro- cedural cues necessary for understanding personalized knowledge, regardless of model capacity. Experiment setup details are provided at Appendix D.5. 6.2 Agent Behavior under Ambiguous Instructions from Users Table 3: Performance under ambiguous queries for personalized knowledge. PC (Percent Com- plete) and SR (Success Rate) (%) indicate how well agents resolve indirect references to person- alized knowledge from memory. Model PC (%) SR (%) GPT-4o (Baseline) 92.0 90.0 GPT-4o 80.4 73.3 Qwen-2.5-72b (Baseline) 75.1 66.7 Qwen-2.5-72b 59.6 53.3While MEMENTO focuses on evaluating personal- ized knowledge grounding with explicit references to prior interactions, real-world human-agent com- munication often involves ambiguous or indirect references. To explore this challenge, we con- ducted a proof-of-concept experiment to assess whether current models can interpret ambiguous instructions that indirectly refer to previously en- countered personalized knowledge.4We created a tailored set of tasks referencing personalized knowledge using contextual cues, synonyms or causal references ( e.g.,Can you set my afternoon tea time routine? →I’m about to enjoy my afternoon tea. Could you set things up as I like them? ). The results, shown in Table 3, reveal the degradation in performance, indicating that handling am- biguous queries remains a key challenge for future personalized embodied agents. We view this as a promising direction for future work, where we plan to systematically extend MEMENTO to evaluate LLM-powered embodied agents’ capabilities under ambiguous and implicit reference scenarios, aiming to better reflect the complexity of real-world human-agent interaction. 4Further details of the dataset and experiment setup are provided in Appendix D.5. 9 7 Conclusion In this work, we present MEMENTO , a novel personalized embodied agent evaluation framework designed to assess the LLM-powered embodied agents’ ability to utilize episodic memory for providing personalized assistance. Our experiments across a range of LLM-powered embodied agents reveal key limitations in their ability to effectively leverage personalized knowledge from memory, particularly when integrating multiple memories and interpreting user patterns. These findings highlight the gap between current capabilities and the demands of real-world personalized assistance. We hope that MEMENTO serves as a stepping stone for future research in developing more effective personalized embodied agents. References [1]C. Agia, | https://arxiv.org/abs/2505.16348v1 |
K. M. Jatavallabhula, M. Khodeir, O. Miksik, V . Vineet, M. Mukadam, L. Paull, and F. Shkurti. Taskography: Evaluating robot task planning over large 3d scene graphs. In Conference on Robot Learning , pages 46–58. PMLR, 2022. [2]M. Ahn, A. Brohan, N. Brown, Y . Chebotar, O. Cortes, B. David, C. Finn, C. Fu, K. Gopalakr- ishnan, K. Hausman, et al. Do as i can, not as i say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691 , 2022. [3]Anthropic. Introducing claude 3.5 sonnet, June 2024. URL https://www.anthropic. com/news/claude-3-5-sonnet . Accessed: 2025-05-07. [4]L. Barsellotti, R. Bigazzi, M. Cornia, L. Baraldi, and R. Cucchiara. Personalized instance- based navigation toward user-specific objects in realistic environments. In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track , 2024. URL https://openreview.net/forum?id=uKqn1Flsbp . [5]Boston Dynamics. Spot robot. https://bostondynamics.com/products/spot/ , 2025. Accessed: 2025-04-28. [6]A. Brohan, N. Brown, J. Carbajal, Y . Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, et al. Rt-2: Vision-language-action models transfer web knowledge to robotic control. arXiv preprint arXiv:2307.15818 , 2023. [7]M. Chang, G. Chhablani, A. Clegg, M. D. Cote, R. Desai, M. Hlavac, V . Karashchuk, J. Krantz, R. Mottaghi, P. Parashar, et al. Partnr: A benchmark for planning and reasoning in embodied multi-agent tasks. arXiv preprint arXiv:2411.00081 , 2024. [8]Y . Chen, J. Arkin, C. Dawson, Y . Zhang, N. Roy, and C. Fan. Autotamp: Autoregressive task and motion planning with llms as translators and checkers. In 2024 IEEE International conference on robotics and automation (ICRA) , pages 6695–6702. IEEE, 2024. [9]J.-W. Choi, Y . Yoon, H. Ong, J. Kim, and M. Jang. Lota-bench: Benchmarking language- oriented task planners for embodied agents. In International Conference on Learning Represen- tations (ICLR) , 2024. [10] C. Clabaugh and M. Matari ´c. Robots for the people, by the people: Personalizing human- machine interaction. Science Robotics , 3(21):eaat7451, 2018. doi: 10.1126/scirobotics. aat7451. URL https://www.science.org/doi/abs/10.1126/scirobotics. aat7451 . [11] Y . Dai, R. Peng, S. Li, and J. Chai. Think, act, and ask: Open-world interactive personalized robot navigation, 2024. URL https://arxiv.org/abs/2310.07968 . [12] P. Das, S. Chaudhury, E. Nelson, I. Melnyk, S. Swaminathan, S. Dai, A. Lozano, G. Kollias, V . Chenthamarakshan, Ji ˇrí, Navrátil, S. Dan, and P.-Y . Chen. Larimar: Large language models with episodic memory control, 2024. URL https://arxiv.org/abs/2403.11901 . [13] K. Dautenhahn. Robots we like to live with?! - a developmental perspective on a personalized, life-long robot companion. In RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No.04TH8759) , pages 17–22, 2004. doi: 10.1109/ROMAN.2004.1374720. 10 [14] L. Downs, A. Francis, N. Koenig, B. Kinman, R. Hickman, K. Reymann, T. B. McHugh, and V . Vanhoucke. Google scanned objects: A high-quality dataset of 3d scanned household items. In2022 International Conference on Robotics and Automation (ICRA) , pages 2553–2560. IEEE, 2022. [15] A. Grattafiori, A. Dubey, A. Jauhri, A. Pandey, A. Kadian, A. Al-Dahle, A. Letman, A. Mathur, A. Schelten, A. Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783 , 2024. [16] Q. Gu, A. Kuwajerwala, S. Morin, | https://arxiv.org/abs/2505.16348v1 |
K. M. Jatavallabhula, B. Sen, A. Agarwal, C. Rivera, W. Paul, K. Ellis, R. Chellappa, et al. Conceptgraphs: Open-vocabulary 3d scene graphs for perception and planning. In 2024 IEEE International Conference on Robotics and Automation (ICRA) , pages 5021–5028. IEEE, 2024. [17] W. Huang, P. Abbeel, D. Pathak, and I. Mordatch. Language models as zero-shot planners: Extracting actionable knowledge for embodied agents. In International conference on machine learning , pages 9118–9147. PMLR, 2022. [18] W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y . Chebotar, et al. Inner monologue: Embodied reasoning through planning with language models. arXiv preprint arXiv:2207.05608 , 2022. [19] W. Huang, F. Xia, D. Shah, D. Driess, A. Zeng, Y . Lu, P. Florence, I. Mordatch, S. Levine, K. Hausman, and brian ichter. Grounded decoding: Guiding text generation with grounded models for embodied agents. In Thirty-seventh Conference on Neural Information Processing Systems , 2023. URL https://openreview.net/forum?id=JCCi58IUsh . [20] A. Huet, Z. B. Houidi, and D. Rossi. Episodic memories generation and evaluation benchmark for large language models. arXiv preprint arXiv:2501.13121 , 2025. [21] A. Hurst, A. Lerer, A. P. Goucher, A. Perelman, A. Ramesh, A. Clark, A. Ostrow, A. Welihinda, A. Hayes, A. Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276 , 2024. [22] I. Kapelyukh and E. Johns. My house, my rules: Learning tidying preferences with graph neural networks. CoRR , abs/2111.03112, 2021. URL https://arxiv.org/abs/2111.03112 . [23] B. Kim, J. Kim, Y . Kim, C. Min, and J. Choi. Context-aware planning and environment- aware memory for instruction following embodied agents. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 10936–10946, October 2023. [24] M. J. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Balakrishna, S. Nair, R. Rafailov, E. Foster, G. Lam, P. Sanketi, et al. Openvla: An open-source vision-language-action model. arXiv preprint arXiv:2406.09246 , 2024. [25] M. K. Lee, J. Forlizzi, S. Kiesler, P. Rybski, J. Antanitis, and S. Savetsila. Personalization in hri: A longitudinal field experiment. In 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI) , pages 319–326, 2012. [26] C. Li, R. Zhang, J. Wong, C. Gokmen, S. Srivastava, R. Martín-Martín, C. Wang, G. Levine, M. Lingelbach, J. Sun, M. Anvari, M. Hwang, M. Sharma, A. Aydin, D. Bansal, S. Hunter, K.-Y . Kim, A. Lou, C. R. Matthews, I. Villa-Renteria, J. H. Tang, C. Tang, F. Xia, S. Savarese, H. Gweon, K. Liu, J. Wu, and L. Fei-Fei. Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation. In K. Liu, D. Kulic, and J. Ichnowski, editors, Proceedings of The 6th Conference on Robot Learning , volume 205 of Proceedings of Machine Learning Research , pages 80–93. PMLR, 14–18 Dec 2023. URL https://proceedings. mlr.press/v205/li23a.html . [27] H. Li, C. Yang, A. Zhang, Y . Deng, X. Wang, and T.-S. Chua. Hello again! LLM-powered personalized agent for long-term dialogue. In L. Chiruzzo, A. Ritter, and L. Wang, editors, Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human | https://arxiv.org/abs/2505.16348v1 |
Language Technologies (Volume 1: Long Papers) , pages 5259–5276, Albuquerque, New Mexico, Apr. 2025. Association for Computational Linguistics. ISBN 979-8-89176-189-6. URL https://aclanthology.org/2025.naacl-long. 272/ . 11 [28] M. Li, S. Zhao, Q. Wang, K. Wang, Y . Zhou, S. Srivastava, C. Gokmen, T. Lee, E. L. Li, R. Zhang, et al. Embodied agent interface: Benchmarking llms for embodied decision making. Advances in Neural Information Processing Systems , 37:100428–100534, 2025. [29] S. Li, X. Puig, C. Paxton, Y . Du, C. Wang, L. Fan, T. Chen, D.-A. Huang, E. Akyürek, A. Anandkumar, J. Andreas, I. Mordatch, A. Torralba, and Y . Zhu. Pre-trained language models for interactive decision-making. In Proceedings of the 36th International Conference on Neural Information Processing Systems , NIPS ’22, 2022. ISBN 9781713871088. [30] J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng. Code as policies: Language model programs for embodied control. In arXiv preprint arXiv:2209.07753 , 2022. [31] Z. Liu, A. Bahety, and S. Song. Reflect: Summarizing robot experiences for failure explanation and correction. arXiv preprint arXiv:2306.15724 , 2023. [32] Manolis Savva*, Abhishek Kadian*, Oleksandr Maksymets*, Y . Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V . Koltun, J. Malik, D. Parikh, and D. Batra. Habitat: A Platform for Embodied AI Research. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2019. [33] Meta. Introducing llama 3.1: Our most capable models to date, July 2024. URL https: //ai.meta.com/blog/meta-llama-3-1/ . Accessed: 2025-05-16. [34] Y . Mu, Q. Zhang, M. Hu, W. Wang, M. Ding, J. Jin, B. Wang, J. Dai, Y . Qiao, and P. Luo. EmbodiedGPT: Vision-language pre-training via embodied chain of thought. In Thirty-seventh Conference on Neural Information Processing Systems , 2023. URL https://openreview. net/forum?id=IL5zJqfxAa . [35] C. Packer, S. Wooders, K. Lin, V . Fang, S. G. Patil, I. Stoica, and J. E. Gonzalez. Memgpt: To- wards llms as operating systems, 2024. URL https://arxiv.org/abs/2310.08560 . [36] J. S. Park, J. C. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein. Generative agents: Interactive simulacra of human behavior. In In the 36th Annual ACM Symposium on User Interface Software and Technology (UIST ’23) , UIST ’23, New York, NY , USA, 2023. Association for Computing Machinery. [37] X. Puig, T. Shu, S. Li, Z. Wang, Y .-H. Liao, J. B. Tenenbaum, S. Fidler, and A. Torralba. Watch-and-help: A challenge for social perception and human-ai collaboration, 2021. URL https://arxiv.org/abs/2010.09890 . [38] X. Puig, E. Undersander, A. Szot, M. D. Cote, T.-Y . Yang, R. Partsey, R. Desai, A. W. Clegg, M. Hlavac, S. Y . Min, et al. Habitat 3.0: A co-habitat for humans, avatars and robots. arXiv preprint arXiv:2310.13724 , 2023. [39] K. Rana, J. Haviland, S. Garg, J. Abou-Chakra, I. Reid, and N. Suenderhauf. Sayplan: Ground- ing large language models using 3d scene graphs for scalable robot task planning. arXiv preprint arXiv:2307.06135 , 2023. [40] N. Reimers and I. Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 , 2019. [41] M. Rueben and W. D. Smart. Privacy in human-robot | https://arxiv.org/abs/2505.16348v1 |
interaction: Survey and future work. We robot , 2016:5th, 2016. [42] G. Sarch, Y . Wu, M. J. Tarr, and K. Fragkiadaki. Open-ended instructable embodied agents with memory-augmented large language models. arXiv preprint arXiv:2310.15127 , 2023. [43] I. Singh, V . Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg. Progprompt: Generating situated robot task plans using large language models. In 2023 IEEE International Conference on Robotics and Automation (ICRA) , pages 11523–11530, 2023. doi: 10.1109/ICRA48891.2023.10161317. 12 [44] C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y . Su. Llm-planner: Few-shot grounded planning for embodied agents with large language models. In Proceedings of the IEEE/CVF international conference on computer vision , pages 2998–3009, 2023. [45] A. Szot, A. Clegg, E. Undersander, E. Wijmans, Y . Zhao, J. Turner, N. Maestre, M. Mukadam, D. S. Chaplot, O. Maksymets, et al. Habitat 2.0: Training home assistants to rearrange their habitat. Advances in neural information processing systems , 34:251–266, 2021. [46] B. Wang, X. Liang, J. Yang, H. Huang, S. Wu, P. Wu, L. Lu, Z. Ma, and Z. Li. Enhancing large language model with self-controlled memory framework, 2024. [47] G. Wang, Y . Xie, Y . Jiang, A. Mandlekar, C. Xiao, Y . Zhu, L. Fan, and A. Anandkumar. V oyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291 , 2023. [48] Z. Wang, B. Yu, J. Zhao, W. Sun, S. Hou, S. Liang, X. Hu, Y . Han, and Y . Gan. Karma: Augmenting embodied ai agents with long-and-short term memory systems. arXiv preprint arXiv:2409.14908 , 2024. [49] J. Wu, R. Antonova, A. Kan, M. Lepert, A. Zeng, S. Song, J. Bohg, S. Rusinkiewicz, and T. Funkhouser. Tidybot: Personalized robot assistance with large language models. Autonomous Robots , 47(8):1087–1102, 2023. [50] Y . Wu, J. Zhang, N. Hu, L. Tang, G. Qi, J. Shao, J. Ren, and W. Song. Mldt: Multi-level decomposition for complex long-horizon robotic task planning with open-source large language model. In International Conference on Database Systems for Advanced Applications , pages 251–267. Springer, 2024. [51] Q. Xie, S. Y . Min, P. Ji, Y . Yang, T. Zhang, K. Xu, A. Bajaj, R. Salakhutdinov, M. Johnson- Roberson, and Y . Bisk. Embodied-rag: General non-parametric embodied memory for retrieval and generation. arXiv preprint arXiv:2409.18313 , 2024. [52] M. Xu, X. Yang, W. Liang, C. Zhang, and Y . Zhu. Learning to plan with personalized preferences. arXiv preprint arXiv:2502.00858 , 2024. [53] W. Xu, Z. Liang, K. Mei, H. Gao, J. Tan, and Y . Zhang. A-mem: Agentic memory for llm agents. arXiv preprint arXiv:2502.12110 , 2025. [54] A. Yang, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Li, D. Liu, F. Huang, H. Wei, et al. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115 , 2024. [55] R. Yang, H. Chen, J. Zhang, M. Zhao, C. Qian, K. Wang, Q. Wang, T. V . Koripella, M. Movahedi, M. Li, et al. Embodiedbench: Comprehensive benchmarking multi-modal large language models for vision-driven embodied agents. arXiv preprint | https://arxiv.org/abs/2505.16348v1 |
arXiv:2502.09560 , 2025. [56] S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y . Cao. React: Synergizing rea- soning and acting in language models. In International Conference on Learning Representations (ICLR) , 2023. [57] S. Yenamandra, A. Ramachandran, K. Yadav, A. Wang, M. Khanna, T. Gervet, T.-Y . Yang, V . Jain, A. W. Clegg, J. Turner, et al. Homerobot: Open-vocabulary mobile manipulation. arXiv preprint arXiv:2306.11565 , 2023. [58] N. Yokoyama, A. Clegg, J. Truong, E. Undersander, T.-Y . Yang, S. Arnaud, S. Ha, D. Batra, and A. Rai. Asc: Adaptive skill coordination for robotic mobile manipulation. IEEE Robotics and Automation Letters , 9(1):779–786, 2023. [59] H. Zhang, W. Du, J. Shan, Q. Zhou, Y . Du, J. B. Tenenbaum, T. Shu, and C. Gan. Build- ing cooperative embodied agents modularly with large language models. arXiv preprint arXiv:2307.02485 , 2023. [60] W. Zhong, L. Guo, Q. Gao, and Y . Wang. Memorybank: Enhancing large language models with long-term memory. arXiv preprint arXiv:2305.10250 , 2023. 13 A Limitations A.1 Limitations Controlled simulator environment. Our experiments are conducted entirely in a controlled simu- lator environment [ 38], which does not fully reflect the complexities of real-world robotics such as perception noise, actuation uncertainty, or unstructured environments. Visual perception is not addressed. Our framework deliberately isolates memory-centric planning by excluding visual perception components such as VLMs [ 55] or VLA [ 6,24] models. This enables focused evaluation of memory utilization, but limits the system’s generalizability to fully grounded perception scenarios. Oracle skills. We intentionally employ oracle skills for low-level perception and motor control to isolate and focus on the high-level planning and memory reasoning capabilities of the LLM-based agents. As a result, our framework does not evaluate the embodied agent’s full task execution process. A.2 Societal Impacts Our work explores how embodied agents can remember and adapt to user-specific preferences through episodic memory, enabling more personalized and natural interactions. This capability has the poten- tial to enhance convenience, efficiency, and user satisfaction in everyday environments—benefiting a wide range of users regardless of age or ability. However, personalization also raises concerns about privacy, bias reinforcement, and over-dependence on AI agents [ 41,25]. Since episodic memory involves storing interaction history, future systems must consider secure and transparent memory handling. Although our study is conducted in a controlled simulator, these considerations will become crucial as such systems move toward real-world deployment. We hope that MEMENTO serves as a foundation for future research in building safe, privacy-aware, and user-aligned personalized embodied agents. B Details of Experiment Setup B.1 Evaluation Method We adopt the official evaluation protocol from the PartNR benchmark [ 7], which provides a python- based framework for assessing multi-step rearrangement tasks. We use this framework without modification. The evaluator analyzes simulator states using three components: (1) propositions that define object relationships to satisfy (e.g., is_on_top([spoon_1], [table_1]) ), (2) dependencies that define temporal conditions for multi-step instructions ( after_satisfied , after_unsatisfied ), and (3) constraints that enforce execution requirements (e.g., ordering, object consistency across steps). We rely on this system to evaluate tasks | https://arxiv.org/abs/2505.16348v1 |
involving ambiguous references and sequential dependencies. The evaluation produces a percent-complete score and binary success indicator. B.2 LLM-Powered Embodied Agent Architecture Following Szot et al. [45], Puig et al. [38], Chang et al. [7], we adopt a two-layer hierarchical control architecture for our LLM-powered embodied agent. We utilize the LLM as a high-level policy planner that selects appropriate skills from the predefined skill library. The selected skill then provides control signals to the simulator. For memory systems, we implement a textual scene-graph as our semantic memory alongside an episodic memory. Skill library. The skill library consists of oracle low-level skills that the LLM policy can select as actions. These action skills are divided into motor skills ( e.g.,navigate ,pick ,place ) and perception skills ( e.g.,describe_object ,find_object ,find_receptacle ). Note that we exclusively used oracle skills in our skill library to focus on episodic memory-centric analysis. Further descriptions are provided in Table 4 and Table 5 14 Table 4: List of available agent motor skills. Skill Description Navigate Used for navigating to an entity. You must provide the name of the entity you want to navigate to. Example: Navigate[counter_22]. Pick Used for picking up an object. You must provide the name of the object. Example: Pick[cup_1]. Place Used for placing an object on a target location. Example: Place[book_0, on, table_2, None, None]. Open Used for opening an articulated entity. Example: Open[chest_of_drawers_1]. Close Used for closing an articulated entity. Example: Close[chest_of_drawers_1]. Explore Doing exploration towards a target object or receptacle, you need to provide the name of the place you want to explore. Wait Used to make agent stay idle for some time. Example (Wait[]) Table 5: List of available agent perception skills. Skill Description FindObjectTool Used to find the exact name/names of the object/objects of interest. If you want to find the exact names of objects on specific receptacles or furnitures, please include that in the query. Example (FindObjectTool[toys on the floor] or FindObjectTool[apples]). FindReceptacleTool Used to know the exact name of a receptacle. A receptacle is a furniture or entity (like a chair, table, bed etc.) where you can place an object. Example (FindRecepta- cleTool[a kitchen counter]). FindRoomTool Used to know the exact name of a room in the house. A room is a region in the house where furniture is placed. Example (FindRoomTool[a room which might have something to eat]). DescribeObjectTool Used to retrieve a brief descriptive or semantic meaning of a given object or furniture name. Example (DescribeObjectTool[sponge_1]). Semantic memory. For semantic memory, we implement a scene-graph style hierarchical represen- tation, which has demonstrated effectiveness for planning problems [ 1,39,16]. Following Chang et al. [7], we utilize a multi-edge directed graph with three distinct levels to represent environmental entities. The top level contains a single root node representing the house environment, the second level comprises room nodes, and the third level encompasses furniture, objects, and agents. Each node stores the corresponding entity’s 3D location and relevant state information. This graph structure is initialized and continuously updated with ground-truth information from the simulator at each state st. In our system, | https://arxiv.org/abs/2505.16348v1 |
this structured semantic memory provides the LLM planner with an interpretable representation of the environment, which can be flexibly queried and reasoned over through natural language descriptions. Episodic memory. Our episodic memory is configured to store the ReAct-style formatting that guides the LLMs’ reasoning process [ 56]. This memory structure captures both the user’s instruction and the complete sequence of <Thought, Action, Observation> triplets generated during task execution. The episodic memory is accessed at the beginning of each task by retrieval and updated upon task completion, enabling the agent to recall previous interactions. Memory retrieval. For retrieval, we encode instructions and memory entries using the all-mpnet- base-v2 sentence transformer [ 40] and use the current task instruction as the query. To avoid ambiguous or irrelevant memories, we retrieve candidate memories only from the history within the same scene as the current task. LLM Setup. We configured the language model with a temperature of 0 to ensure deterministic outputs. For sampling parameters, we set top_p to 1 and top_k to 50. B.3 Computing resources Our experiments primarily utilized commercial API services rather than local computing resources. We used OpenAI’s Chat API for accessing GPT-4o, Claude models through Anthropic’s API, and OpenRouter’s API service for accessing Llama-3.1 [ 33], Qwen-2.5 [ 54]. For running simulation 15 environment we used 8 NVIDIA GeForce RTX 3090 GPUs. For our implementation and evaluation, we use Huggingface library2, vLLM library. Both libraries are licensed under Apache License, Version 2.0. And we used langchain library, under MIT License. We have confirmed that all of the artifacts used in this paper are available for non-commercial scientific use. C Details of M EMENTO C.1 Personalized Knowledge We categorize knowledge about personal items as object semantics and knowledge about consistent behaviors as user patterns to structure our evaluation approach. Object semantics can be further classified into four sub-categories: naive ownership ( e.g., "my cup"), object preference ( e.g., "a chessboard I play chess with my brother"), history ( e.g., "graduation gift from my grandma"), and group ( e.g., "my favorite toys", where toys indicate toy airplane and toy truck). User patterns encompass consistent action sequences in specific contexts, with two sub-categories: personal routine (e.g., "my remote work setup") and arrangement preference ( e.g., "my movie night setup"). Based on these personalized knowledge categories, we designed tasks that specifically require agents to recall and apply this information to evaluate their memory utilization capabilities. Further details are provided in Table 6 and Table 7 Table 6: List of the subcategories for object semantics. Type Description Example Ownership Possessive reference to the user’s belonging My cup, My laptop Preference Object aligned with the user’s individual taste or selec- tionBread from my favorite bakery, Jug for serving drink History Object linked to personal memory or past experience photo of the my beloved pet, travel souvenir vase Groups Conceptual or functional grouping of multiple related objectsmy home office setup, my travel essentials Table 7: List of the subcategories for user patterns. Type Description Example Routine A sequence or setup the user follows as a habit or regular | https://arxiv.org/abs/2505.16348v1 |
activity.meal time setting, setup for cooking routine Preference A specific way the user prefers to prepare or arrange their environment when a particular situation occurs.my coffee break, cozy decora- tion spot C.2 PartNR Dataset PartNR is designed to evaluate planning and reasoning capabilities in embodied tasks and is recog- nized for its comprehensive collection of natural language instructions in household environments. The benchmark includes four primary task types: (1) constraint-free basic rearrangement tasks, (2) spatial tasks requiring reasoning about object positions, (3) temporal tasks with sequential dependen- cies, and (4) heterogeneous tasks involving actions that can only be performed by human agents. We selected PartNR episodes specifically for their complexity beyond simple pick-and-place operations. The rich linguistic structure and diverse task requirements make PartNR particularly suitable for evaluating user patterns personalization, enabling us to create scenarios that effectively test an agent’s ability to adapt to user-specific communication patterns. C.3 Dataset Statistics The dataset comprises 438 episodes, divided into two main stages. The memory acquisition stage contains 201 episodes (89 object semantics tasks and 112 user patterns tasks). The memory utilization stage also contains 201 single-memory episodes (89 object semantics and 112 user patterns tasks), along with 36 multi-memory episodes, which include 12 object semantics pairs, 12 user patterns pairs, and 12 mixed pairs. All episodes were constructed using the Habitat 3.0 simulator. 16 C.4 Data Generation Process To create stage-specific tasks, we leveraged GPT-4o and systematic process to incorporate personal- ized knowledge into existing task structures. Captioning process. Since we employ LLMs as high-level planners for embodied agent, we generated natural language descriptions of the objects at the scene using GPT-4o, to enable agents to reason over object descriptions without relying on direct visual perception. We collected object models from the OVMM dataset [ 57], and used GPT-4o to generate natural language descriptions from rendered object images. Especially, the Google Scanned dataset [ 14], included within OVMM, provides object identities in file names. We leveraged this additional information alongside the images to produce more realistic descriptions. Through this process, we generated 1,920 object descriptions with 66 categories of objects. The prompts used to generate the object descriptions are provided in Appendix E. Preprocessing scenes. To collect the suitable episodes for our purposes, we preprocessed and filtered episodes. First, we only gathered non-heterogeneous episodes, which should be executed by human agents. Second, we filtered out tasks where the target object was not uniquely specified ( e.g., “Bring one apple to the kitchen table”), as our task setting requires identification and reference of a specific object based on personalized knowledge that distinguishes it from other similar objects. Third, in cases where captions for target handles were unavailable, we substituted alternative objects. If no objects from the same category were available, we excluded the entire episode from our dataset. Distractor sampling. To sample distractor objects for episodes focusing on object semantics, we utilized PartNR’s dataset generation methods. This approach allowed us to systematically select objects located adjacent to target objects on the same receptacles or floor surfaces, requiring agents to differentiate between objects of the | https://arxiv.org/abs/2505.16348v1 |
same category. Figure 7: Episodes with zero success rate (31 in total) were excluded from the analysis.Details of task instruction generation. For instruc- tion generation, we prompted GPT-4o to generate personalized knowledge tasks based on the knowl- edge categories defined in Section C.1. For tasks involving object semantics, we provided the captions of the target object pairs along with instructions to guide GPT-4o in generating natural object semantics descriptions. (e.g., instruction: "Bring the cup on the kitchen table", object description: "a white mug with fancy handle", personalized knowledge: "The mug is my coffee mug.") We first instructed the model to create the most plausible subcategory of object seman- tics, which was then used to generate personalized knowledge specific to the objects. In these object se- mantics cases, the instruction comprised three components: command instruction (e.g., "Bring the cup on the kitchen table"), additional information (e.g., "The cup is a white mug with fancy handle."), and personalized knowledge (e.g., "That mug is my coffee mug"). For user patterns tasks, we provided instructions to GPT-4o and allowed the model to infer the most plausible user patterns corresponding to a sequence of actions. For the memory acquisition stage instruction, we also concatenated the command instruction (e.g., "Bring the cup and dish from the kitchen table to living room") with personalized knowledge (e.g., "That’s my dinner setup"). Detailed information about the prompts we used is provided in Appendix E. Quality control. After generating episodes for MEMENTO , we implemented a two-stage filtering process for quality control. First, we applied heuristic filtering to eliminate episodes with potentially confusing distractor objects in the same scene (e.g., episodes using identical object handles or requiring similar personalized knowledge, which could create ambiguity). Second, we tested each episode with GPT-4o and excluded any episode where GPT-4o failed five consecutive attempts, indicating potential issues with task feasibility or clarity. (Figure 7) This quality control process resulted in filtering out 31 episodes (13.4% of the total dataset) that exhibited consistently poor performance. Specifically, we identified: 31 episodes with zero success rate, indicating complete 17 task failure. The remaining 201 episodes showed strong performance metrics, with 95% confidence intervals of [0.755, 0.844] for success rate and [0.823, 0.893] for completion rate. The high correlation (r = 0.908) between success rate and completion rate indicates consistent performance across different evaluation metrics. These high-quality episodes served as the foundation for generating golden trajectories, which were subsequently used in our memory quality analysis and discussion experiments. By establishing GPT-4o’s successful task executions as golden trajectories, we ensure that our evaluation framework is based on demonstrably achievable performance standards, providing a reliable benchmark for assessing memory quality and discussion effectiveness. D Additional Experiments & Analysis D.1 Knowledge Type-based Analysis. Additional experiment results show the performance of small models—Llama3.1-8b and Qwen2.5- 7b—on knowledge type-based analysis (Figure 8). Similar to other frontier models, they struggled with utilizing episodic memory on tasks requiring recognition of user patterns. For tasks requiring object semantics during the memory acquisition stage, we found that these small models struggle to understand the user’s intent to differentiate | https://arxiv.org/abs/2505.16348v1 |
objects with DescribeObjectTool and rely primarily on commonsense reasoning, instead of describing the objects. Figure 8: Memory acquisition stage and memory utilization stage all models. D.2 Knowledge Type-based Analysis on Dual-Memory Tasks Figure 9 shows the success rate of each model on dual-memory tasks, compared with the discrete success/failure outcomes when executing each corresponding episode individually during the memory acquisition stage. We can observe that almost all models tend to struggle with utilizing user patterns knowledge from different memory sources, with smaller models (Llama3.1-8b and Qwen2.5-7b) demonstrating particularly pronounced difficulties. D.3 Memory Quality Analysis Experiment setup. We further analyze the effect of memory quality by comparing gold memory, consisting of successful and shortest-path trajectories that serves as high-quality references with the memory obtained from interaction histories in the declaration stage . By evaluating performance differences between these two settings, we aim to understand how memory quality affects agent performance across the memory utilization stage for both tasks (single-memory tasks, joint-memory tasks). 18 Figure 9: Personalized knowledge type-based analysis on joint-memory tasks, comparing with memory acquisition stage’s corresponding episodes. Figure 10: The results of memory quality analysis. Performance degradation with lower-quality trajectories. Figure 10 shows the performance comparison between gold memory and retrieved memory across the memory utilization stage . In thememory utilization stage , high-capacity models (Llama-3.1-70b) show relatively stable perfor- mance across gold and retrieved memory, while lower-capacity models (Llama-3.1-8b) exhibit a substantial drop when using retrieved memory. This suggests that less capable models have more difficulty extracting relevant information from an imperfect memory context. When executing more demanding joint-memory tasks, which require combining and reasoning across multiple memory sources, performance degrades sharply across all models with retrieved memory. This indicates that the challenge of integrating multiple memories introduces compounding complexity, as the agent must not only retrieve relevant information but also correctly synthesize disparate memory contexts. These findings emphasize a fundamental limitation of retrieval-based memory systems; while gold memory provides ground truth references, retrieved memory is inherently prone to semantic noise, as it relies on approximate similarity rather than precise matching. Enhancing memory quality through more precise filtering is essential to improve reasoning performance, particularly for complex joint tasks that require integration of multiple memory sources, and especially for smaller models with limited reasoning capabilities. 19 D.4 Success and Error Case Analysis As shown in Figure 6, we sampled success and error cases in memory utilization stage. Tasks require object semantics knowledge. Success cases demonstrate that agents can effectively reference personalized object attributes from episodic memory, correctly identifying and applying the specific information needed for task completion. ( A.1) However, we observed distinct error patterns when agents needed to utilize this type of knowledge: missed personalization cues ( B.1), where agents failed to recognize the need to access personalized knowledge; hallucinations ( B.2), where agents fabricated non-existent attributes; and memory recall failures ( B.3), where agents were unable to locate relevant information despite its presence in the provided context. Tasks require user patterns. For user patterns tasks, we observed that agents employed two distinct strategies: commonsense reasoning ( C.1) treating | https://arxiv.org/abs/2505.16348v1 |
memory as exemplars for step-by-step reasoning, and direct reference ( C.2) for distinctive patterns like "my go-to breakfast." However, both approaches introduced specific vulnerabilities leading to two common failure patterns. First, com- monsense reasoning ( D.1) happened when agents attempted to apply the reasoning-based approach but encountered gaps they couldn’t bridge, leading them to substitute commonsense knowledge that seemed plausible but contradicted established personalized routines. Second, inaccurate recall (D.2) occured when agents recognized the need for personalized knowledge but retrieved imprecise or incomplete information from memory. The sequential nature of these approaches made them particularly error-prone, as mistakes at any intermediate step propagated through subsequent actions. This vulnerability explains the consistently higher failure rates in user patterns tasks across all models, highlighting fundamental challenges in maintaining coherence through multi-step reasoning over episodic memory. D.5 Details of Discussion Section Experiments Experiment setup details for Section 6.1. We sampled 30 episodes by selecting 10 episodes from each of three scenes, with careful balancing across task types and difficulty levels informed by our preliminary experimental results. To minimize the influence of erroneous trajectories, we provided GPT-4o-generated gold memory as the given episodic memory, thereby reducing the impact of noisy interaction histories. We also used GPT-4o to generate summaries of the action trajectories, including user instructions, to facilitate compact memory representations. For each evaluation, one exemplar shot was given to the agent, and top- kmemory entries were retrieved based on the current query, where k= 10 for frontier models (GPT-4o and Qwen-2.5-72B) and k= 7for smaller models (LLaMA-3.1-8B and Qwen-2.5-7B), considering context length limitations. The prompts used for these experiments are provided in Appendix E.3. Experiment setup details for Section 6.2. We sampled 30 episodes, selecting 10 episodes from each of three scenes, and tailored them by modifying the personalized knowledge statements and reference components. To mitigate potential bias, all episodes were jointly created and validated by two authors. For each model, we first generated memory traces during the memory acquisition stage and then evaluated memory usage during the utilization stage. Table 8 provides representative examples of the ambiguous instruction pairs used. In each case, the user rephrases the original request to refer indirectly to previously established personalized knowledge, simulating natural variability in real-world human-agent communication. Table 8: Examples of ambiguous instruction modifications in the additional experiment dataset. Original Memory Utilization Instruction Rephrased Ambiguous Instruction Can you arrange my cozy reading setup on the dining table ?Can you set up the dining table as I prefer so I canread the book comfortably ? Could you help me tidy up by moving my grad- uation gifted book , the candle with a brown and blue gradient, and the round white clock with black numbers to the stand?Could you help me tidy up by moving my book received to celebrate completing my studies , the candle with a brown and blue gradient, and the round white clock with black numbers to the stand? 20 E Prompts E.1 Prompts for Dataset Generation Object Semantics Instruction Gener ation Pr omptobject_semantics: |- Y our task is to gener ate a user | https://arxiv.org/abs/2505.16348v1 |
instruction that includes object semantics for an embodied agent that can perform rearr angement tasks. The instruction should be grounded in personalized object-level semantics based on the original instruction and object descriptions. The object semantics can be categorized into 4 types: - ownership: Indicates that the user personally owns or has a special claim on an object. - preference: Indicates the user's specific preferences related to an object (e.g., placement, condition). - history: Reflects the user's past inter action or meaningful history with the object. - group: Defines a logical or personalized grouping of multiple objects (e.g., “m y coffee set” for mug + saucer + spoon). Y ou should gener ate 2 types of instructions and object semantics: - Stage 1: Instruction for memorization; The instruction should include the original instruction, descriptions of all the objects, and explicit object semantics of the relevant objects. This will be used to store memory . - Stage 2: Instruction for utilization; The instruction should require the agent to understand and use the previously stored object semantics. It should sound natur al to humans and be difficult for an agent without access to memory . K eep it short and situated. Relevant objects must be referred to only using their stored semantics, without an y descriptive attributes. F or all other objects, refer to them using visual or descriptive attributes. - Object Semantics: This is the semantic information associated with each object used in the instruction. Only include the most relevant one object semantics based on the instruction context. Not all target objects need to be included, but use as man y of them as possible. Note that if the original instruction involves a sequence of object inter actions, that order should be preserved in the Stage 2 instruction. The output format should be as follows: 21 [Example] ### Input - original_instruction: <original instruction> - handle_info: <list of the objects with short descriptions> ### Output - Stage 1: <instruction> + <object descriptions> + <object semantics> - Stage 2: <instruction with object semantics formed in a natur al wa y> - Used Object: <List about the used objects' categories> - Object Semantics: <Object semantics category about the relevant objects> [Example 1] {shot_examples} ... ### Input - original_instruction: {instruction} - handle_info: {handle_info}User Pattern Instruction Gener ation Pr omptuser_pattern: |- Y our task is to gener ate a user instruction that includes user pattern for embodied agent that can perform rearr angment tasks. The instruction should be related to personalized knowledge based on the original instruction. The user pattern can be categorized into 2 types: - preference: A specific wa y the user prefers to prepare or arr ange their environment when a particular situation occurs. - routine: A sequence or setup the user follows as a habit or regular activity . Y ou should gener ate 2 types of instructions, memory , and user pattern: - Stage 1: Instruction for memorization; The instruction should be original instruction + user pattern. Y ou should explicitly state the user's preference or routine in the instruction. - Stage 2: Instruction | https://arxiv.org/abs/2505.16348v1 |
for utilization; The instruction should be only about user's preference or routine that a human would natur ally use in the situated environment. Y ou should mak e the instruction difficult for the agent without using memory and try to mak e it short.22 - User pattern: The user pattern should be the user's preference or routine that can be reused for future rearr angement tasks. Note that if the original instruction requires a sequence of actions, the order of the actions should be followed for the stage 2 instruction. The output format should be as follows: ### Input <original instruction> ### Output - Stage 1: <original instruction> + <user pattern> - Stage 2: <user pattern formed in a natur al wa y> - Memory: <Memory about user's preference or routine> - User pattern: <user pattern> [Example 1] {shot_examples} ... ### Input {org_instruction}Captioning Pr ompt for O VMM Objectscaptioning: > Gener ate a short, but precise caption for the given object. F ocus only on the object, ignoring the back ground. Include its type, primary colors, and an y distinctive features. Examples: {shot_examples} Image: Category: {category} Image: {image} 23 Captioning Pr ompt for Captioning_Google Objectscaptioning_google: > Gener ate a short, but precise caption for the given object. F ocus only on the object, ignoring the back ground. Include its type, primary colors, and an y distinctive features. If you can 't recognize the object, refer to the name of the objects I gave. Examples: {shot_examples} Image: Category: {category} Name: {name} Image: {image}24 E.2 Prompts for Agent Zer o Shot Agent ReAct Pr omptprompt: |- {system_tag}Y ou are an agent that solves embodied-agent planning problems. The task assigned to you will be situated in a house and will gener ally involve navigating to objects, picking and placing them on different receptacles to achieve rearr angement. Y ou strictly follow an y format specifications and pa y attention to the previous actions tak en in order to avoid repeating mistak es. If there are multiple tasks to complete, please follow them in the order they appear in the instruction. Rooms do not need to be explored more than once. This means if you have explored the living room and have not found the object, then you should explore the kitchen, if a relevant object is still not found, you should explore the hallwa y etc... Man y calls to the same action in a row are a sign that something has gone wrong and you should try a different action.{eot_tag} {r ag_examples} {user_tag}T ask: {input} {world_description} Possible A ctions: {tool_descriptions} - Done: Used to indicate that the agent has finished the task. Example (Done[]) What is the next action to mak e progress towards completing the task? Return your response in the following format Thought: <reasoning for wh y you are taking the next action> <next action call> Assigned! 25 Here is an example: Thought: Since there are no objects found I should explore a room I have not explored yet. Explore[<room name>] Assigned! {eot_tag}{assistant_tag}26 E.3 Prompts for Discussion Summary for Section 6.1 Pr | https://arxiv.org/abs/2505.16348v1 |
omptsummary: |- Y ou are a helpful assistant designed to summarize episodic task execution tr aces of an embodied agent. Y ou will be given a full tr ace of the agent's actions, thoughts, and results as it attempts to follow a human instruction. Please output a compact memory par agr aph including: - Instruction: Cop y exactly the instruction from the tr ace. This is the sentence just before the first Thought appears. (Try to understand user's intention well.) - Plan: Briefly summarize the k ey high-level steps the agent performed. Guidelines: - Use 2 to 3 short sentences. - Do not list low-level micro actions. - Ignore repeated failures unless they affected the outcome. - Do not invent an y details not present in the tr ace. - Use past tense and third-person style. [Example 1] Input: {input_tr ace_example} Output: {output_example} .... [Example] Input: {input_tr ace} Output: 27 F Extended Related Work Recent research has increasingly emphasized the integration of memory mechanisms into large language model (LLM) agents to support long-term reasoning, planning, and personalization. Park et al. [36] propose Generative Agents , which simulate human-like behavior by maintaining a memory stream of past experiences in natural language. This enables agents to reflect, retrieve, and plan based on their individual histories. Similarly, Xu et al. [ 53] introduce A-Mem , a dynamic memory system inspired by Zettelkasten, which structures memory as evolving and interconnected notes that the agent can generate, retrieve, and update over time, supporting agentic autonomy and adaptability. Personalization in dialogue agents has also been explored through memory-enhanced architectures. Li et al. [ 27] present a personalized dialogue agent that leverages both short-term and long-term mem- ories to maintain user-specific context across sessions, significantly improving response consistency and contextual relevance. Wang et al. [ 46] propose the Self-Controlled Memory (SCM) framework, where an agent dynamically decides when and what to store or retrieve from memory, leading to improved coherence and knowledge retention over extended interactions. Zhong et al. [ 60] introduce MemoryBank , a structured long-term memory module that enhances LLMs by storing and retrieving relevant interaction history to support consistent and personalized user responses across multiple turns. Das et al. [ 12] present Larimar , a framework that integrates episodic memory control into LLMs, enabling selective recall and forgetting to improve memory scalability and privacy-aware reasoning. More broadly, Packer et al. [ 35] conceptualize memory management as an OS-level abstraction with MemGPT , enabling an LLM to autonomously manage and interact with its internal and external memory hierarchies. This work highlights how memory can act as a core architectural layer to enable scalable, autonomous agents capable of long-horizon tasks and continuous learning. G License For our implementation and evaluation, we use Huggingface library5and vLLM library. Both libraries are licensed under Apache License, Version 2.0. We have confirmed that all of the artifacts used in this paper are available for non-commercial scientific use. G.1 License for the Assets The existing assets used in this research are properly credited and their licenses respected: •Habitat [38, 45, 32]: MIT License | https://arxiv.org/abs/2505.16348v1 |
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization Pierre Achkar1, Tim Gollub2and Martin Potthast3 1Leipzig University, Fraunhofer ISI Leipzig 2Bauhaus-Universität Weimar 3Kassel University, hessian.AI, ScaDS.AI Abstract The exponential growth of scientific publications has made it increasingly difficult for researchers to stay updated and synthesize knowledge effectively. This paper presents XSum , a modular pipeline for multi-document summarization (MDS) in the scientific domain using Retrieval-Augmented Generation (RAG). The pipeline includes two core components: a question-generation module and an editor module. The question-generation module dynamically generates questions adapted to the input papers, ensuring the retrieval of relevant and accurate information. The editor module synthesizes the retrieved content into coherent and well-structured summaries that adhere to academic standards for proper citation. Evaluated on the SurveySum dataset, XSum demonstrates strong performance, achieving considerable improvements in metrics such as CheckEval, G-Eval and Ref-F1 compared to existing approaches. This work provides a transparent, adaptable framework for scientific summarization with potential applications in a wide range of domains. Code available at https://github.com/webis- de/scolia25-xsum/tree/main Keywords Multi-document Summarization (MDS), Retrieval-Augmented Generation (RAG), Scientific Literature Summariza- tion 1. Introduction The rapid growth of scientific literature has made it increasingly difficult for researchers to stay up-to-date with the latest developments. The number of papers published each month has increased exponentially since 1994, with fields such as artificial intelligence (AI) doubling their research output [ 1]. While this growth reflects the progress of research communities, it also presents a serious challenge: how can researchers stay informed and extract key insights from this volume of information? This overload of information makes it difficult to manually read, understand, and summarize the growing body of literature. This challenge becomes paramount in rapidly evolving fields such as AI, where researchers often need to synthesize knowledge from multiple sources in order to make progress. Summarizing research is not simply reading through papers but also identifying the most important information, connecting ideas from different sources, and presenting them in a clear and concise way. Automated summarization solutions are essential to help researchers save time and focus on the core information. One promising approach to this challenge is Multi-Document Summarization (MDS), which combines information from multiple sources into clear and concise summaries. The concept itself is not new; for example, early work from 1999 proposed to use reference relationships between scientific papers to generate survey-style summaries [2]. The approach identifies key fragments of cited papers, analyzes similarities and differences between them, and classifies citation contexts to support summarization. Over time, summarization methods have evolved from static approaches to deep learning models and later to pre-trained language models (PLMs) [ 3]. Currently, the field is dominated by Large Language Models (LLMs), which are pre-trained on massive datasets and capable of generating high-quality text. Retrieval-Augmented Generation (RAG) builds on these advances by combining retrieval techniques with LLMs, enabling systems to find relevant information and synthesize it into accurate and coherent answers. A typical RAG pipeline processes a set of documents 𝐷={𝑑1, 𝑑2, . . . , 𝑑 𝑛}by dividing them SCOLIA 2025, the First International Workshop on Scholarly Information Access, ECIR | https://arxiv.org/abs/2505.16349v1 |
2025, 10th April 2025, Lucca, Italy /orcid0009-0007-0791-9078 (P. Achkar); 0000-0003-1737-6517 (T. Gollub); 0000-0003-2451-0665 (M. Potthast) ©2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).arXiv:2505.16349v1 [cs.CL] 22 May 2025 into smaller chunks, encoding them into dense vector embeddings with a pre-trained model, and storing them in a vector database for later retrieval. In the context of MDS, a search query to the vector database acts as a summarization guideline that can either be provided by the user or the MDS system. When a query is provided, the top- 𝑘most relevant chunks are retrieved based on similarity metrics and passed to an LLM, which generates responses grounded in the retrieved content. Despite the recent advances, summarizing scientific literature remains an open research problem, requiring not only linguistic fluency and coherence, but also robust relevance and adherence to academic standards for citing literature correctly. To address these challenges, we present XSum , a RAG pipeline designed for MDS in the scientific domain. XSum builds upon the typical RAG pipeline and introduces two new innovative components: a question-generation module and an editor module. The question- generation module formulates questions on the basis of the input papers to be summarized, which are then passed to the RAG component. The editor module synthesizes the set of answers retrieved from the RAG component into coherent summaries, ensuring that the resulting output is comprehensive, reliable, and well-structured. The proposed pipeline is evaluated on the SurveySum [4] dataset, which is designed to test MDS methods in the scientific domain. The results show that XSum outperforms existing methods on metrics such as CheckEval [ 5], G-Eval [ 6] and Ref-F1, demonstrating its ability to produce high-quality summaries. We consider the quality of a generated summary to be defined by its ability to comprehensively cover the essential content of the source documents, to maintain a coherent and fluent narrative, and to accurately reflect the original citations. 2. Related Work The task of summarizing multiple scientific documents has evolved considerably over time. Early approaches, such as SciSumm , introduced query-driven summarization by clustering relevant text segments from co-cited papers to generate contextualized summaries [ 7]. These methods leveraged citation relationships but struggled with complex content relationships across documents. Later developments introduced neural network-based architectures for MDS. For instance, HiMAP andHierSumm utilized hierarchical models and passage ranking techniques to enhance content selection and fusion, resulting in more coherent and contextually relevant summaries [ 8,9]. These methods marked a shift from purely extractive approaches to more integrative models capable of generating fluent summaries. The integration of extraction and abstraction further refined summarization methods. Shinde et al. proposed a hybrid pipeline that combines BERT-based extractive models with BigBird-PEGASUS for abstractive summarization, achieving robust performance in the biomedical domain [ 10]. Similarly, KGSum introduced knowledge graph-based encoding to model document content and relationships, employing a two-stage decoding strategy to produce focused and cohesive summaries [11]. The field has taken a major step forward with the emergence of retrieval augmented generation (RAG) pipelines. OpenScholar1, for example, demonstrated a | https://arxiv.org/abs/2505.16349v1 |
novel approach by integrating a specialized datastore of 45 million papers with iterative retrieval and feedback loops, enabling precise, citation- backed responses, highlighting growing interest in retrieval augmented systems [ 12]. Another approach to MDS using retrieval is proposed through the SurveySum framework, which introduces two pipelines, Pipeline 1 and Pipeline 2, both integrating retrieval-based selection with LLM-based summarization [4]. These pipelines are evaluated on the SurveySum dataset, a benchmark specifically designed for MDS in scientific literature, which consists of survey sections paired with their cited papers. This is the same dataset used in this work, and a more detailed discussion of its structure will be provided in the Evaluation section. Pipeline 1 uses a neural ranking approach where full-text papers are segmented into overlapping chunks during pre-processing. These chunks are ranked by monoT5-3B , which assigns relevance scores based on the title of the target survey section. The highest-ranked chunks are then passed to an LLM, such as GPT-4 , to generate the final summary. Pipeline 2, on the other hand, relies on embedding-based 1https://openscilm.allen.ai/ retrieval, where text chunks are represented as dense embeddings using SPECTER22and stored in a FAISS vector database. The section title (e.g. Data Generation via PLM:Explaining Models’ Decisions) is used as a query to retrieve relevant chunks at inference time. Unlike Pipeline 1, which directly selects the top-ranked chunks for summarization, Pipeline 2 includes a re-ranking step where an LLM evaluates and ranks the retrieved content before summarization. The resulting chunks are then summarized into a cohesive section. Figures 1 and 2 illustrate the structures of these pipelines. While these pipelines achieve acceptable performance, they rely on static retrieval using section titles as queries, which can limit adaptability to different summarization contexts. Among them, Pipeline 2 is more comparable to our approach XSum , as it utilizes embedding-based retrieval rather than direct ranking. However, XSum addresses key limitations by introducing a question generation module that dynamically formulates structured questions based on the title and abstract of the input papers, which serve as queries during retrieval, thereby improving retrieval relevance. Additionally, it features an editor module that synthesizes retrieved content into a coherent, citation-rich summary, ensuring better fluency, accuracy, and adherence to academic writing standards. The complete pipeline and the functionality of these components will be explained in detail in the Methodology section. Reference PapersDocument Pre-Processing LLMFinal SummaryFull Text Chunks RankingRelevant Chunks Figure 1: Overview of Pipeline 1: The system segments full-text papers into overlapping chunks, ranks them using monoT5-3B based on the section title, and selects the top-ranked chunks for LLM-based summarization. Reference PapersFAISS Document Pre-Processing RAGFinal SummaryFull Text Section TitleChunksRelevant ChunksReranking Figure 2: Overview of Pipeline 2: Instead of ranking with a neural model, this pipeline encodes chunks as dense embeddings using SPECTER2 , stores them in a FAISS vector database, retrieves them based on section title queries, and applies reranking before LLM-based summarization. Beyond SurveySum , several other datasets have been developed for MDS, particularly in the biomedical domain. Datasets such as Cochrane-auto andMS2 focus on summarizing clinical trials and systematic reviews, providing benchmarks for | https://arxiv.org/abs/2505.16349v1 |
evaluating summarization methods in evidence-based medicine [13,14]. Another relevant dataset is Multi-XScience , which was initially considered for evaluating the proposed approach, as it focuses on synthesizing related work sections from abstracts and cited references [ 15]. However, a preliminary analysis revealed missing values in the reference papers used to generate the related work sections, raising concerns about its completeness for reliable benchmarking. Furthermore, while related work sections can be considered multi-document summaries, they are often shaped by the comparative and argumentative nature of the paper’s contributions rather than being purely extractive or abstractive. Given these considerations, the SurveySum dataset appeared to be a more appropriate choice for evaluating our approach, as it explicitly focuses on summarizing multiple scientific papers into structured survey sections. To our knowledge, no other experimental work has been conducted on SurveySum beyond the evaluations presented by its authors so far. 3. Methodology This section introduces the XSum pipeline, a modular approach to summarizing scientific literature into coherent and traceable outputs. The initial idea for building this pipeline was inspired by the interview 2https://huggingface.co/allenai/specter2 paradigm, where an interviewer interacts with a domain expert. In this analogy, the interviewer prepares a structured set of questions based on the expert’s domain knowledge, conducts the interview in which the expert answers these questions, and finally an editor compiles the conversation into a well-structured summary. This concept motivated the design of XSum and led to the introduction of two key modules: a question generation module, which formulates structured questions to guide the retrieval process, and an editor module, which synthesizes the retrieved answers into a coherent and citation-rich summary. Each module plays an important role in ensuring that the summaries generated are both relevant and well-structured, as described in the following subsections. 3.1. Overview of the Pipeline The proposed pipeline for MDS, XSum , illustrated in Figure 3, transforms input reference papers into a coherent summary through a sequence of modular steps. It begins by using the titles and abstracts of the reference papers to generate broad and general questions using an LLM. These questions, designed to reflect the main themes and contributions of the papers, are stored for later use. The full texts of the reference papers are then processed by dividing them into manageable chunks, which are embedded in dense vector representations and stored in a vector database. This pre-processing ensures efficient retrieval of relevant content in subsequent stages. The stored questions are then used to query the database and retrieve the most relevant chunks. The retrieval process involves an initial similarity-based ranking of the chunks, followed by a re-ranking step to refine their relevance. The final set of retrieved chunks is paired with the corresponding questions. These question-chunk pairs are then passed to an LLM, which generates concise answers based on the retrieved content. If the context is insufficient, the LLM will refrain from generating an answer, ensuring accuracy and credibility. Finally, the set of question-answer pairs is passed to the editor module, which synthesizes them into a comprehensive and well-structured summary. The editor ensures coherence, logical flow, and | https://arxiv.org/abs/2505.16349v1 |
adherence to academic standards while incorporating citations to maintain traceability. LLM Question GenerationReference PapersFAISS Document Pre-Processing RAGFinal Summary Question Answering Final Summary GenerationLLMFull Text Title + AbstractQuestion Q&A PairsChunks QuestionsRelevant Chunks Reranking Figure 3: Overview of the XSum Pipeline. The pipeline processes reference papers into summaries through modular steps. Document Pre-Processing segments papers into chunks, encodes them as embeddings, and stores them in a FAISS database. Question Generation uses an LLM to generate questions from titles and abstracts. In Question Answering, a RAG framework retrieves relevant chunks and generates answers with an LLM. Finally, the Editor Module (Final Summary Generation) synthesizes the answers into a coherent, citation-rich summary. 3.2. Question Generation Module This module is essential for aligning the retrieval and summarization stages with the specific content of the reference documents. By leveraging the generative capabilities of LLMs, it ensures that the pipeline is driven by structured, contextually relevant queries. The approach draws on insights from methods such as HyDe [16],HyQE [17] and reverse HyDe [18], all of which use generative techniques to improve retrieval relevance. HyQE (Hypothetical Document Embeddings) involves generating hypothetical content based on a query, encoding this content into embeddings, and then using these embeddings to improve retrieval accuracy. Both HyQE (Hypothetical Query Embeddings) and reverse HyDe follow a similar strategy, but focus on generating hypothetical questions or queries that match the content of a document. These hypothetical questions bridge the semantic gap between queries and retrievable content, improving the ranking of relevant results [17]. In our pipeline, the title and abstract of each reference paper serve as input to a pre-trained LLM, which generates 𝑘= 5 broad and semantically rich questions encapsulating the core themes and contributions of the paper. These questions are stored as structured queries for subsequent stages. The generated questions serve two primary functions: first, they refine the retrieval process by ensuring that only the most contextually relevant content is retrieved; second, they provide a structured framework to guide the subsequent synthesis and summarization phases. For illustration, examples of such generated questions can be found in Appendix A.1. 3.3. Document Pre-processing The pre-processing phase ensures that the reference papers are prepared for efficient retrieval and summarization by arranging them in a format suitable for downstream tasks. This phase consists of three main steps: •Chunking Documents: The full texts of the reference papers are divided into interconnected chunks of 150 tokens each, with an overlap of 20 tokens. This overlap preserves contextual continuity between successive chunks, while respecting sentence boundaries ensures that the division does not disrupt the semantic flow of the text. We determined this configuration by experimentation, after trying different setups, finding that it provided the best balance between contextual preservation and computational efficiency. •Embedding Generation: Each chunk is encoded into dense vector representations using the SPECTER2 model, which is specifically designed to capture the semantic relationships and con- textual meanings in academic texts. •Vector Database Indexing: Chunks are indexed in FAISS , a high-speed similarity search database, for efficient retrieval. 3.4. Question Answering Module In this module, the focus is on integrating retrieval | https://arxiv.org/abs/2505.16349v1 |
and synthesis to generate concise, contextually relevant answers to the questions formulated in the previous stage. By combining robust retrieval techniques with an LLM in a RAG framework, this module ensures that the pipeline produces high- quality output that is grounded in the source material. Questions are embedded into dense vector representations using the same SPECTER2 model used in the document pre-processing phase. The retrieval process proceeds in two stages: 1.Initial Retrieval: Using cosine similarity, the top 100 chunks most relevant to each question are retrieved from the FAISS vector database, serving as an initial filtering step. 2.Reranking: The retrieved chunks are re-ranked using the ColBERT2 model [ 19], which evaluates token-level interactions between the question and the chunks. This refinement step ensures that the 20 most relevant chunks are selected. The final set of 20 chunks is presented to a pre-trained LLM along with the corresponding question. The LLM synthesizes a coherent and accurate response based solely on the retrieved context. If the retrieved chunks do not provide sufficient information, the LLM is instructed not to generate an answer, minimizing unsupported or speculative output. To ensure credibility and traceability, the LLM includes valid citations from the retrieved chunks in its responses. By grounding the answers in the source material, this module adheres to academic standards and facilitates the verification of the generated content. 3.5. Final Summary Generation (Editor Module) The Editor Module synthesizes the answers generated in the previous step into a cohesive and com- prehensive summary, aggregating all question-answer pairs into a unified narrative that reflects the overarching themes and contributions of the papers. A pre-trained LLM is used as the editor to generate the final summary. The model is prompted to write an extensive, coherent summary that seamlessly integrates the individual answers while maintaining a logical flow. The Editor LLM ensures the sum- mary adheres to academic standards. It incorporates citations into the final summary, ensuring that all statements are properly grounded in the retrieved source material. The prompt used in this module is as follows: Editor Module Prompt ### CONTEXT ### You are writing the final script of an interview with an expert on the topic ’{topic}’. The final script should summarize the key insights and findings from the questions and answers provided. Keep the target audience in mind, which includes researchers, students, and professionals in the field. ### QUESTIONS AND ANSWERS ### {questions_and_answers} ### INSTRUCTIONS ### Include the most relevant and important points discussed. Be aware of plagiarism, i.e., you should not copy the text, but use them as inspiration. Avoid using markdown formatting in the text. Avoid splitting into subsections, or creating an introduction and conclusion for it. Avoid introducing new information and focus on summarizing the existing content. Always include the citations (e.g., [BIBREF14], [BIBREF16]) mentioned in the answers in the final section. 4. Evaluation The proposed pipeline is evaluated using a domain-specific dataset for MDS. This section details the dataset, metrics, implementation, results, examples, and discussion, providing a comprehensive analysis of the pipeline’s performance. 4.1. Dataset The evaluation of the proposed pipeline is conducted | https://arxiv.org/abs/2505.16349v1 |
on the SurveySum3dataset, a domain-specific resource designed for MDS tasks in scientific literature. This dataset includes 79 survey sections across fields such as AI, natural language processing (NLP), and machine learning (ML). Each section is paired with the full-text content of its cited papers, with an average of 7.38 papers cited per section. The dataset is explicitly designed to test MDS models on the synthesis of content from multiple sources, making it particularly suited for assessing the proposed pipeline. 4.2. Metrics The evaluation employs a mix of traditional and LLM-based metrics to assess the quality of summaries in terms of content coverage, coherence, and citation alignment: 3https://github.com/unicamp-dl/surveysum ROUGE (Recall-Oriented Understudy for Gisting Evaluation) [ 20] measures the overlap between the generated summaries and the reference text. It calculates n-gram overlap, word sequence matching, and the longest common subsequences. ROUGE-1, ROUGE-2, and ROUGE-L are used in this study to capture unigrams, bigrams, and sentence-level matches, respectively. BERTScore [21] evaluates semantic similarity between the generated and reference summaries using contextual embeddings from PLMs like BERT. Reference F1 Score (Ref-F1) measures how accurately the citations in the generated summaries align with those in the ground truth. It computes precision (proportion of correctly included references) and recall (proportion of ground-truth references captured in the generated summary), and combines them into an F1 score. This metric is essential in scientific summarization, where attribution and citation accuracy are critical. G-Eval4[6] is a framework for evaluating the output of natural language generation (NLG) using LLMs, providing reference-free assessments based on criteria such as coherence, coverage, fluency, and relevance. It uses Chain-of-Thought (CoT) reasoning to systematically generate detailed evaluation steps, ensuring consistency and robustness in scoring. Scores are assigned on a fixed scale (e.g. 1 to 5) and refined using token probabilities, enabling granular, continuous analyses that capture the nuances between outputs. By bypassing the need for reference outputs, it is particularly effective for tasks where predefined references are unavailable or impractical, such as creative or open-ended text generation. Experiments show that G-Eval achieves a stronger correlation with human judgments than traditional metrics such as ROUGE, as well as neural evaluators, on benchmarks such as SummEval [22] for summarization. CheckEval5[5] is a robust evaluation framework that uses LLMs to evaluate generated text using a structured checklist-based approach. It supports two evaluation modes: reference-based, which compares the generated text to reference summaries, and criteria-driven, which evaluates the text against predefined dimensions such as coherence, fluency, and coverage. By breaking down evaluation criteria into detailed sub-aspects, framed as Boolean (yes/no) questions, CheckEval simplifies the evaluation process and increases its reliability and interpretability. The framework operates in three stages: aspect selection, where key evaluation dimensions are identified; checklist generation, where detailed questions are created and refined; and checklist-based evaluation, where LLMs respond to the questions, with the final score calculated as the proportion of positive responses. Validated against the SummEval benchmark, CheckEval demonstrates high correlation with human judgment and strong inter-annotator agreement. ROUGE and BERTScore evaluations report recall scores to emphasize content coverage, while G-Eval and CheckEval focus on the coverage criterion, consistent | https://arxiv.org/abs/2505.16349v1 |
with SurveySum’s methodology for assessing core content representation. 4.3. Implementation Details This section outlines the tools, models, and frameworks utilized in our development process: •Development Environment: The pipeline was implemented in Python, utilizing sentence-transformers for embedding generation, nltk for text processing, and FAISS for efficient vector-based retrieval. All experiments were conducted on a Tesla V100-PCIE-32GB GPU, enabling efficient embedding generation, chunk retrieval, and summarization tasks. •Pre-trained LLMs: For text generation, we employed gpt4o-mini_15-2-2024-preview , while Phi-3-small-8k-instruct was utilized for evaluation. Both models were configured with a temperature of 0.3 and a top-p of 0.95 to ensure controlled and consistent outputs. This choice aligns with the position that the same model should not be used for both generation and evaluation to mitigate potential bias. Research has highlighted that using identical or equally 4https://github.com/nlpyang/geval 5https://github.com/jayralencar/check-eval Table 1 Comprehensive performance comparison of the evaluated pipelines based on traditional metrics (ROUGE, BERTScore, Ref-F1) and LLM-based metrics (G-Eval, CheckEval). Pipeline ROUGE-1 ROUGE-2 ROUGE-L BERTScore Ref-F1 G-Eval CheckEval Pipeline_1 0.42 0.08 0.19 0.57 0.64 3.1 0.61 Pipeline_2 0.49 0.10 0.23 0.59 0.72 4.0 0.76 XSum 0.51 0.10 0.24 0.62 0.76 4.2 0.97 powerful models for both tasks can lead to skewed results, as LLMs like GPT-4 tend to favor their own outputs due to egocentric biases [ 23]. Notably, the SurveySum paper does not specify whether identical models were used in their study for both tasks. 4.4. Results Since the original SurveySum paper did not report ROUGE and BERTScore metrics for the benchmark pipelines, we calculated these values. G-Eval and CheckEval were also computed using our implementa- tion to ensure consistency, employing the Phi-3-small-8k-instruct model as the evaluator. This approach guarantees a fair comparison across all pipelines, enabling a comprehensive assessment of XSum’s performance relative to the benchmarks. Any differences in the results (particularly for G-Eval and CheckEval) can be attributed to differences in the evaluation settings and model configurations compared to the original SurveySum experiments. For clarity, Pipeline_1 andPipeline_2 are the two pipelines that performed best in the SurveySum experiments. The results, summarized in Table 1, highlight XSum’s consistent outperformance of the benchmark pipelines across all metrics. It achieves ROUGE-1 (0.51) and ROUGE-L (0.24), reflecting its ability to effectively capture unigrams and sentence-level structures. Its BERTScore (0.62) highlights its strong semantic alignment with reference summaries, reflecting its capability to retain content integrity through paraphrasing and semantic rephrasing. Furthermore, XSum attains the highest Ref-F1 (0.76), G-Eval (4.2), and CheckEval (0.97) scores, emphasizing its superiority in generating coherent, relevant, and high-quality summaries. To further demonstrate the performance of XSum, we present two examples of summaries generated by it, along with their corresponding ground truth (original section text) and evaluation scores. These examples have been selected based on their performance across the evaluation metrics, one representing the highest average score across all metrics and the other representing the lowest average score. Due to their length, the full examples are provided in Appendix A.2. 4.5. Discussion The strong performance of XSum is largely driven by its two key features: the question-generation module and the editor module. By dynamically generating queries about | https://arxiv.org/abs/2505.16349v1 |
the document content, the retrieval module ensures relevant and contextual results, addressing the limitations of static query approaches such as using section titles, as in Pipeline_2 . Additionally, the use of ColBERT as a reranker may contribute to better chunk retrieval by prioritizing the most relevant and informative sections during the ranking process. The editor module further enhances the pipeline by synthesizing retrieved information into coherent summaries with proper citations, resulting in outputs that adhere to academic standards. The quality of the content generated by XSum highlights its ability to synthesize multiple sources into a structured and coherent summary. The selected examples illustrate both the strengths and limitations of the approach. The high-scoring example (Example 1) closely follows the human-written text, effectively capturing key technical details while maintaining logical flow and factual consistency. This suggests that XSum can generate summaries that are both informative and well-structured and in line with academic standards. However, a notable difference remains in the style and clarity of the summaries. Human-written sections tend to be more compact and nuanced, often presenting a comparative perspective that sets different contributions in relation to each other. In contrast, XSum summaries tend to be verbose, often providing extended explanations and additional contextual information beyond what is strictly necessary for summarizing. This is particularly evident in the low-scoring example (Example 2), where the generated text, while factually accurate, lacks the same level of selectivity as the human-written version, including an unnecessary degree of background detail rather than focusing solely on comparative insights. This contrast highlights a key challenge in scientific summarization. Although retrieval-driven methods such as XSum excel at aggregating and structuring information, they do not yet fully replicate the complex synthesis and prioritization that domain experts perform when writing a summary of multiple related papers. Nevertheless, XSum still produces highly structured and factually based summaries, demonstrating that automated MDS can be a valuable tool for scientific literature synthesis, particularly in assisting researchers with information overload. Despite its strengths, the low ROUGE-2 scores across all pipelines highlight a common problem in abstractive summarization: achieving bigram overlap with reference summaries. RAG-based pipelines, including XSum, prioritize semantic richness and coherence over strict lexical matching, which reduces alignment with reference summaries. However, ROUGE-1 and ROUGE-L scores show moderate align- ment, reflecting the ability to capture essential unigrams and sentence-level structures. BERTScore, which assesses semantic similarity, achieves satisfactory results, highlighting the ability of such pipelines to capture the essence of content through paraphrasing and semantic rephrasing, even when lexical overlap is limited. It is important to note that while the ROUGE metrics provide valuable insights into lexical overlap and content coverage, they are inherently limited in assessing the nuances of abstractive summarisation. This limitation is addressed by incorporating a set of metrics - BERTScore, G-Eval and CheckEval - that more effectively capture semantic similarity, coherence and overall quality. In addition to traditional metrics, frameworks like G-Eval and CheckEval provide refined assessments of summary quality by leveraging LLMs to evaluate coherence, relevance, and coverage. These metrics excel at capturing semantic and structural attributes that conventional metrics often | https://arxiv.org/abs/2505.16349v1 |
overlook, making them particularly effective for evaluating abstractive summaries. However, their dependence on specific LLMs introduces challenges of consistency and reproducibility, as evaluation outcomes may vary with different model configurations. This highlights the need for standardization in LLM-driven evaluation practices. Finally, XSum’s modular design offers substantial flexibility in adapting to different summarization tasks. The question-generation module can be customized to generate domain-specific or task-specific questions to improve relevance in different contexts. Similarly, the editor module allows customization of tone, style, and abstraction levels, enabling outputs to be tailored for different audiences, from academic researchers to professional practitioners. This adaptability ensures the pipeline’s scalability and applicability to a wide range of domains, addressing the growing demand for efficient MDS in complex settings. 5. Conclusion and Future Work This work addresses the challenges of MDS in the scientific domain by introducing a modular RAG- based pipeline featuring two key enhancements: a question-generation module and an editor module. These components enable the pipeline to synthesize information from multiple scientific papers into cohesive, well-structured summaries. Experimental evaluations on the SurveySum dataset demonstrate considerable improvements in metrics such as CheckEval, G-Eval, and Ref-F1 compared to existing approaches. By providing detailed guidance on the design and implementation of RAG-based pipelines, this work contributes to making these systems more transparent, reproducible, and adaptable for diverse summarization tasks. While the current pipeline achieves strong performance, several opportunities for future improve- ments remain. A key direction is evaluating XSum against other MDS pipelines to enable a more in-depth comparison of effectiveness and retrieval quality. Such comparisons would provide insights into the relative strengths and limitations of different summarization strategies. Additionally, con- ducting an ablation study would allow for a deeper understanding of the impact of each component in the pipeline, particularly the question-generation module and the editor module, to assess their individual contributions to overall performance. Optimizing data ingestion pipelines, which are often a bottleneck in large-scale industrial applications (as emphasized in systems like ColPali [24]), could further enhance scalability and efficiency. Moreover, integrating vision-language models to process visually rich documents, including text, tables, and figures, offers a promising direction for improving retrieval accuracy and extending the system’s capabilities to more complex scientific datasets. 6. Limitations Despite its contributions, this work has several limitations that warrant further investigation: •Scalability and Real-World Deployment: While the proposed pipeline demonstrates strong performance in controlled environments, this work does not address the challenges of scaling the pipeline for real-world applications. Issues such as handling extremely large datasets, ensuring low latency, and optimizing cost-effective deployment for different organizational needs remain unaddressed and require further research. •Qualitative Analysis: While quantitative evaluations on metrics like CheckEval and G-Eval demonstrate strong performance, this study lacks a comprehensive qualitative analysis of the generated summaries. •Document Retrieval Scope: The pipeline assumes a predefined set of input papers for sum- marization and does not address the challenge of identifying or retrieving relevant documents for a specific topic. This limitation highlights the need for further research into integrating robust document retrieval mechanisms with summarization workflows to enhance the pipeline’s applicability. References [1]M. Krenn, | https://arxiv.org/abs/2505.16349v1 |
L. Buffoni, B. C. Coutinho, S. Eppel, J. G. Foster, A. Gritsevskiy, H. Lee, Y. Lu, J. P. Moutinho, N. Sanjabi, R. Sonthalia, N. M. Tran, F. Valente, Y. Xie, R. Yu, M. Kopp, Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network, Nat. Mac. Intell. 5 (2023) 1326–1335. URL: https://doi.org/10.1038/ s42256-023-00735-0. doi: 10.1038/S42256-023-00735-0 . [2]H. Nanba, M. Okumura, Towards multi-paper summarization using reference information, in: T. Dean (Ed.), Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI 99, Stockholm, Sweden, July 31 - August 6, 1999. 2 Volumes, 1450 pages, Morgan Kaufmann, 1999, pp. 926–931. URL: http://ijcai.org/Proceedings/99-2/Papers/038.pdf. [3]H. Zhang, P. S. Yu, J. Zhang, A systematic survey of text summarization: From statistical methods to large language models, CoRR abs/2406.11289 (2024). URL: https://doi.org/10.48550/arXiv.2406. 11289. doi: 10.48550/ARXIV.2406.11289 .arXiv:2406.11289 . [4]L. C. Fernandes, G. B. Guedes, T. S. Laitz, T. S. Almeida, R. F. Nogueira, R. A. Lotufo, J. Pereira, Sur- veysum: A dataset for summarizing multiple scientific articles into a survey section, CoRR abs/2408.16444 (2024). URL: https://doi.org/10.48550/arXiv.2408.16444. doi: 10.48550/ARXIV. 2408.16444 .arXiv:2408.16444 . [5]Y. Lee, J. Kim, J. Kim, H. Cho, P. Kang, Checkeval: Robust evaluation framework using large language model via checklist, CoRR abs/2403.18771 (2024). URL: https://doi.org/10.48550/arXiv. 2403.18771. doi: 10.48550/ARXIV.2403.18771 .arXiv:2403.18771 . [6]Y. Liu, D. Iter, Y. Xu, S. Wang, R. Xu, C. Zhu, G-eval: NLG evaluation using GPT-4 with better human alignment, CoRR abs/2303.16634 (2023). URL: https://doi.org/10.48550/arXiv.2303.16634. doi:10.48550/ARXIV.2303.16634 .arXiv:2303.16634 . [7]N. Agarwal, R. S. Reddy, K. Gvr, C. P. Rosé, SciSumm: A multi-document summarization system for scientific articles, in: S. Kurohashi (Ed.), Proceedings of the ACL-HLT 2011 System Demonstrations, Association for Computational Linguistics, Portland, Oregon, 2011, pp. 115–120. URL: https: //aclanthology.org/P11-4020/. [8]A. Fabbri, I. Li, T. She, S. Li, D. Radev, Multi-news: A large-scale multi-document summa- rization dataset and abstractive hierarchical model, in: A. Korhonen, D. Traum, L. Màrquez (Eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Lin- guistics, Association for Computational Linguistics, Florence, Italy, 2019, pp. 1074–1084. URL: https://aclanthology.org/P19-1102/. doi: 10.18653/v1/P19-1102 . [9]Y. Liu, M. Lapata, Hierarchical transformers for multi-document summarization, in: A. Korhonen, D. Traum, L. Màrquez (Eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Florence, Italy, 2019, pp. 5070–5081. URL: https://aclanthology.org/P19-1500/. doi: 10.18653/v1/P19-1500 . [10] K. Shinde, T. Roy, T. Ghosal, An extractive-abstractive approach for multi-document sum- marization of scientific articles for literature review, in: A. Cohan, G. Feigenblat, D. Freitag, T. Ghosal, D. Herrmannova, P. Knoth, K. Lo, P. Mayr, M. Shmueli-Scheuer, A. de Waard, L. L. Wang (Eds.), Proceedings of the Third Workshop on Scholarly Document Processing, Asso- ciation for Computational Linguistics, Gyeongju, Republic of Korea, 2022, pp. 204–209. URL: https://aclanthology.org/2022.sdp-1.25/. [11] P. Wang, S. Li, K. Pang, L. He, D. Li, J. Tang, T. Wang, Multi-document scientific summariza- tion from a knowledge graph-centric view, in: N. Calzolari, C. Huang, H. Kim, J. Pustejovsky, L. Wanner, K. Choi, P. Ryu, H. Chen, L. Donatelli, H. Ji, S. Kurohashi, P. Paggio, N. Xue, S. Kim, Y. Hahm, Z. He, T. K. Lee, E. Santus, F. | https://arxiv.org/abs/2505.16349v1 |
Bond, S. Na (Eds.), Proceedings of the 29th International Conference on Computational Linguistics, COLING 2022, Gyeongju, Republic of Korea, October 12-17, 2022, International Committee on Computational Linguistics, 2022, pp. 6222–6233. URL: https://aclanthology.org/2022.coling-1.543. [12] A. Asai, J. He, R. Shao, W. Shi, A. Singh, J. C. Chang, K. Lo, L. Soldaini, S. Feldman, M. D’Arcy, D. Wadden, M. Latzke, M. Tian, P. Ji, S. Liu, H. Tong, B. Wu, Y. Xiong, L. Zettlemoyer, G. Neubig, D. S. Weld, D. Downey, W. Yih, P. W. Koh, H. Hajishirzi, Openscholar: Synthesizing scientific literature with retrieval-augmented lms, CoRR abs/2411.14199 (2024). URL: https://doi.org/10. 48550/arXiv.2411.14199. doi: 10.48550/ARXIV.2411.14199 .arXiv:2411.14199 . [13] J. Bakker, J. Kamps, Cochrane-auto: An aligned dataset for the simplification of biomedical abstracts, in: M. Shardlow, H. Saggion, F. Alva-Manchego, M. Zampieri, K. North, S. Štajner, R. Stodden (Eds.), Proceedings of the Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024), Association for Computational Linguistics, Miami, Florida, USA, 2024, pp. 41–51. URL: https://aclanthology.org/2024.tsar-1.5/. doi: 10.18653/v1/2024.tsar-1.5 . [14] J. DeYoung, I. Beltagy, M. van Zuylen, B. Kuehl, L. L. Wang, MS2: multi-document summa- rization of medical studies, CoRR abs/2104.06486 (2021). URL: https://arxiv.org/abs/2104.06486. arXiv:2104.06486 . [15] Y. Lu, Y. Dong, L. Charlin, Multi-xscience: A large-scale dataset for extreme multi-document summarization of scientific articles, in: B. Webber, T. Cohn, Y. He, Y. Liu (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, Association for Computational Linguistics, 2020, pp. 8068–8074. URL: https://doi.org/10.18653/v1/2020.emnlp-main.648. doi: 10.18653/V1/2020.EMNLP-MAIN.648 . [16] L. Gao, X. Ma, J. Lin, J. Callan, Precise zero-shot dense retrieval without relevance labels, CoRR abs/2212.10496 (2022). URL: https://doi.org/10.48550/arXiv.2212.10496. doi: 10.48550/ARXIV. 2212.10496 .arXiv:2212.10496 . [17] W. Zhou, J. Zhang, H. Hasson, A. Singh, W. Li, Hyqe: Ranking contexts with hypothetical query embeddings, CoRR abs/2410.15262 (2024). URL: https://doi.org/10.48550/arXiv.2410.15262. doi:10.48550/ARXIV.2410.15262 .arXiv:2410.15262 . [18] Y. Gao, Y. Xiong, X. Gao, K. Jia, J. Pan, Y. Bi, Y. Dai, J. Sun, Q. Guo, M. Wang, H. Wang, Retrieval-augmented generation for large language models: A survey, CoRR abs/2312.10997 (2023). URL: https://doi.org/10.48550/arXiv.2312.10997. doi: 10.48550/ARXIV. 2312.10997 .arXiv:2312.10997 . [19] K. Santhanam, O. Khattab, J. Saad-Falcon, C. Potts, M. Zaharia, Colbertv2: Effective and efficient retrieval via lightweight late interaction, CoRR abs/2112.01488 (2021). URL: https://arxiv.org/abs/ 2112.01488. arXiv:2112.01488 . [20] C.-Y. Lin, ROUGE: A package for automatic evaluation of summaries, in: Text Summarization Branches Out, Association for Computational Linguistics, Barcelona, Spain, 2004, pp. 74–81. URL: https://aclanthology.org/W04-1013/. [21] T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, Y. Artzi, Bertscore: Evaluating text generation with BERT, CoRR abs/1904.09675 (2019). URL: http://arxiv.org/abs/1904.09675. arXiv:1904.09675 . [22] A. R. Fabbri, W. Kryscinski, B. McCann, C. Xiong, R. Socher, D. R. Radev, Summeval: Re-evaluating summarization evaluation, Trans. Assoc. Comput. Linguistics 9 (2021) 391–409. URL: https: //doi.org/10.1162/tacl_a_00373. doi: 10.1162/TACL\_A\_00373 . [23] Z. Li, X. Xu, T. Shen, C. Xu, J. Gu, Y. Lai, C. Tao, S. Ma, Leveraging large language models for NLG evaluation: Advances and challenges, in: Y. Al-Onaizan, M. Bansal, Y. Chen (Eds.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024, Miami, FL, USA, November 12-16, 2024, Association for Computational Linguistics, 2024, | https://arxiv.org/abs/2505.16349v1 |
pp. 16028–16045. URL: https://aclanthology.org/2024.emnlp-main.896. [24] M. Faysse, H. Sibille, T. Wu, B. Omrani, G. Viaud, C. Hudelot, P. Colombo, Colpali: Efficient document retrieval with vision language models, CoRR abs/2407.01449 (2024). URL: https://doi. org/10.48550/arXiv.2407.01449. doi: 10.48550/ARXIV.2407.01449 .arXiv:2407.01449 . A. Appendix A.1. Examples of Generate Questions Below are examples of generated questions produced by our question-generation module. Generated Questions Example 1 Paper Title: Automatic melody harmonization with triad chords: A comparative study Paper Abstract: Several prior works have proposed various methods for the task of automatic melody harmonization, in which a model aims to generate a sequence of chords to serve as the harmonic accompaniment of a given multiple-bar melody sequence. In this paper, we present a comparative study evaluating and comparing the performance of a set of canonical approaches to this task, including a template matching based model, a hidden Markov based model, a genetic algorithm based model, and two deep learning based models. The evaluation is conducted on a dataset of 9,226 melody/chord pairs we newly collect for this study, considering up to 48 triad chords, using a standardized training/test split. We report the result of an objective evaluation using six different metrics and a subjective study with 202 participants. Generated Questions: •What are the key differences in performance among the various models evaluated for automatic melody harmonization? •How does the dataset of 9,226 melody/chord pairs contribute to the robustness of the study’s findings? •What specific metrics were used for the objective evaluation of the models, and how do they compare in terms of effectiveness? •What insights were gained from the subjective study involving 202 participants regarding the perceived quality of the harmonizations? •What future directions for research in automatic melody harmonization does this study suggest based on its findings? Generated Questions Example 2 Paper Title: Virtuosonet: A hierarchical rnn-based system for modeling expressive piano performance Paper Abstract: In this paper, we present our application of deep neural networks to modeling piano performance, which imitates the expressive control of tempo, dynamics, articulations, and pedaling from pianists. Our model consists of recurrent neural networks with hierarchical attention and a conditional variational autoencoder. The model takes a sequence of note-level score features extracted from MusicXML as input and predicts piano performance features of the corresponding notes. To render musical expressions consistently over long-term sections, we first predict tempo and dynamics at the measure level and, based on the result, refine them at the note level. The evaluation through listening tests shows that our model achieves a more human-like expressiveness compared to previous models. We also share the dataset used for the experiment. Generated Questions: •What are the key components of the hierarchical RNN architecture used in Virtuosonet for modeling expressive piano performance? •How does the model differentiate between measure-level and note-level predictions in terms of tempo and dynamics? •What specific features from MusicXML are utilized as input for the model, and how do they contribute to the performance output? •In what ways does the evaluation through listening tests demonstrate the model’s superiority in expressiveness compared to previous approaches? •What insights can be drawn from | https://arxiv.org/abs/2505.16349v1 |
the dataset shared in the paper regarding the training and evaluation of deep music generation models? A.2. Evaluation Examples Evaluation Example 1 Survey Title: AMMUS:A Survey of Transformer-based Pretrained Models in Natural Language Processing Survey Section: Taxonomy:Extensions:Long-Sequence T-PTLMs Evaluation Scores: ROUGE-1: 0.61, ROUGE-2: 0.19, ROUGE-L: 0.32, BERTScore: 0.69, Ref-F1: 1.00, G-Eval: 4.8, CheckEval: 1.00 Ground Truth (Section Text): The self-attention attention module in transformers updates the representation of each input token by attending to all tokens in the input sequence. The quadratic time complexity of the self-attention module limits the application of T-PTLMs to long input sequences. To overcome this drawback, self-attention variants like sparse self-attention and linearized self-attention are proposed to reduce its complexity and hence extend T-PTLMs to long input sequences also BIBREF205 . Some of the popular T-PTLMs based on a) sparse self-attention are Longformer BIBREF206 , ETC BIBREF207 , BigBird BIBREF208 and Reformer BIBREF209 and b) linearized self-attention are Performer BIBREF210 . Sparse self-attention reduces the complexity by including sparsity bias which reduces the number of query-key pairs that each query attends to. In linearized self-attention, reduced complexity is achieved by disentangling the attention with kernel feature maps and then computing the attention in reverse order. Generated Content (XSum Output): The exploration of Transformer-based pretrained models, particularly in the context of long- sequence processing, has revealed significant advancements and innovations across various archi- tectures. Key modifications to the original Transformer model have emerged, including Adaptive Computation Time (ACT), hierarchical structures, and dynamic halting mechanisms, all aimed at en- hancing computational efficiency and representation capabilities for complex inputs [BIBREF205]. The introduction of models like Longformer, with its linear scaling attention mechanism and local- global attention integration, demonstrates a marked improvement in handling lengthy documents compared to traditional self-attention approaches, outperforming models such as RoBERTa in tasks requiring extensive context [BIBREF206]. The Extended Transformer Construction (ETC) architecture further addresses the challenges of scaling input length and encoding structured data through its global-local attention mechanism and the use of relative position encodings, which significantly enhance the model’s performance on tasks like HotpotQA and WikiHop [BIBREF207].Similarly, BigBird’s sparse attention mechanism allows for efficient processing of long sequences while maintaining the representational power of traditional Transformers, achiev- ing state-of-the-art results in question answering and summarization tasks [BIBREF208]. The Reformer model introduces techniques such as Locality-Sensitive Hashing (LSH) attention and reversible residual layers, which contribute to its memory efficiency and ability to handle long sequences effectively, demonstrating comparable performance to standard Transformers across various NLP tasks [BIBREF209].Performers leverage the FAVOR+ mechanism to achieve nearly linear time complexity and sub-quadratic memory consumption, enhancing both efficiency and accuracy in large-scale applications, including protein sequence modeling and pixel-prediction tasks [BIBREF210]. These advancements collectively indicate a promising trajectory for future research, emphasizing the need for continued exploration of efficient architectures and pre-training strategies that can further enhance the capabilities of Transformer-based models in processing long sequences and complex data structures across diverse domains. Evaluation Example 2 Survey Title: Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey Survey Section: Data Generation via PLM:Explaining Models’ | https://arxiv.org/abs/2505.16349v1 |
Decisions Evaluation Scores: ROUGE-1: 0.35, ROUGE-2: 0.05, ROUGE-L: 0.13, BERTScore: 0.63, Ref-F1: 0.35, G-Eval: 4.5, CheckEval: 0.11 Ground Truth (Section Text): Despite the impressive performance of deep learning models for various NLP tasks, a remaining challenge to widespread adoption is the lack of explanations for the models’ decisions. This hinders the development and debugging process, as well as user trust. This is especially true for application domains such as healthcare, security, and online education. As such, a considerable number of approaches have been proposed for explaining deep learning models’ behavior, including model-intrinsic BIBREF220 , BIBREF221 , BIBREF222 and model-agnostic approaches BIBREF223 , BIBREF224 , BIBREF225 . While model-intrinsic explanations expose internal model state (e.g. feature importance or attention scores), in model-agnostic (post-hoc) methods, explanations are generated via the model predictions without inspecting the internal state. Generative models are often applied for post-hoc explanations, aiming to obtain either counterexamples BIBREF226 , BIBREF227 , BIBREF228 or natural language texts BIBREF229 , BIBREF230 , BIBREF231 for explaining purposes. Generating counterexamples can shed light on the decision boundaries of the models (i.e. explaining when a model changes its decision), thus improving intepretability. To this end, the generated counterexamples should be close to the decision boundaries so that small modifications result in changing the model predictions. Traditionally, heuristic rules applied to the original inputs create likely counterexamples BIBREF227 , BIBREF232 , BIBREF233 , BIBREF234 . PLMs have been leveraged to generate more diverse examples for better evaluation BIBREF235 , BIBREF228 , BIBREF236 . In particular, BIBREF228 proposes a method based on GPT-2 to generate counterfactuals that are close to the original sentences and entail specific relationships with the original, facilitating label induction (e.g. negation, insertion, shuffle). Concretely, an input sentence is concatenated with a relation label (e.g. negation) and a template consisting of the special tokens [BLANK] to form the prompt for GPT-2 model. For instance, for the sentence ˘201c It is great for kids änd the relation label ˘201c negate¨, the following prompt is constructed: ˘201c It is great for kids. [negation] It is [BLANK] great for [BLANK]. [SEP]¨. Next, the GPT-2 model generates answers for the [BLANK] in the template (e.g. ˘201c not [ANSWER] children ˘201d, separated by the special token [ANSWER] ). To fine-tune the GPT-2 model, non-parallel datasets (e.g. CommonGen, Natural Questions and SQuAD) are automatically processed to find the relations between pairs of sentences and to construct the templates for each relation based on the obtained pairs. It is worth noting that the sentences generated by GPT-2 might have the same label as the original input sentence. In addition, BIBREF228 show that the generated counterexamples can be helpful to improve the performance of the downstream models, e.g. for natural language inference, duplicate question detection, and sentiment analysis. Other research is informing the task of natural language explanation generation , where the goal is to expose the rationale behind the model decisions in automatically generated natural language text. Any approach must critically require that the generated response is faithful to the model behavior. To this end, BIBREF230 propose to first generate the explanations, | https://arxiv.org/abs/2505.16349v1 |
and then employ the explanations to obtain the final model predictions. They use natural language inference as the task requiring explanations. Label-specific GPT-2 models are fine-tuned over concatenations of corresponding premises, hypotheses, and human-provided explanations, so that at inference, the model generates an explanation based on premise and hypothesis. Next, the explanations together with the premise and the hypothesis are consumed by an explanation processor model (e.g. RoBERTa) to select the most likely label. This process obtains a more faithful explanation for the label choice, compared to traditional prediction-first approaches BIBREF229 . However, this approach does not provide explanations that reference non-selected labels. To address the question of why other labels are not chosen, BIBREF231 exploit counterexamples, deriving them from original samples with heuristic rules. The original samples and counterexamples are provided to GPT-2 to generate an explanation for the question ˘201c Why A not B ˘201d. Generated Content (XSum Output): The exploration of data generation and model explanation through large pre-trained language models (PLMs) has revealed critical insights into the challenges and advancements in the field. A significant challenge in the interpretability of machine learning models, particularly in natural language processing (NLP), lies in the complexity of the models themselves. Explanations must be comprehensible to users with varying levels of expertise, necessitating a balance between accuracy and interpretability. Techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) have emerged as effective methods for generating explanations that enhance user trust in model predictions. LIME has demonstrated high recall rates and usability, enabling users to better assess the trustworthiness of individual predictions, while SHAP offers a theoretically robust framework that aligns closely with human intuition, improving the interpretability of complex models [BIBREF220], [BIBREF221].Trust is a pivotal factor in the deployment of machine learning models, as it influences user reliance on model predictions. Explanations play a crucial role in fostering this trust by elucidating the rationale behind predictions, thereby enabling users to make informed decisions. The experiments con- ducted with human subjects highlighted that while explanations can enhance understanding, they may also reveal underlying issues with model predictions, leading to a decrease in trust when users discern that predictions are based on untrustworthy reasons [BIBREF220]. Furthermore, the model-agnostic nature of explanation methods like LIME and SHAP allows for their applica- tion across various classifiers, facilitating feature engineering and improving model performance [BIBREF220], [BIBREF221].Recent advancements in instancewise feature selection and the use of mutual information have further enhanced model interpretation by providing tailored explanations for specific predictions. This approach contrasts with traditional global feature importance metrics, allowing for a more nuanced understanding of model behavior [BIBREF222]. The introduction of multimodal explanation frameworks, which combine visual and textual justifications, has also shown promise in improving interpretability by leveraging the strengths of different modalities [BIBREF223]. Additionally, the development of datasets such as ACT-X and VQA-X has provided valuable resources for evaluating multimodal explanations in activity recognition and visual ques- tion answering tasks, thereby advancing the field of explainable AI [BIBREF223].The integration of counterfactual explanations, particularly through frameworks like GYC and MiCE, has introduced | https://arxiv.org/abs/2505.16349v1 |
arXiv:2505.16381v1 [cs.CL] 22 May 2025PaTH Attention: Position Encoding via Accumulating Householder Transformations Songlin Yang1Yikang Shen2Kaiyue Wen3Shawn Tan2 Mayank Mishra2Liliang Ren4Rameswar Panda2Yoon Kim1 1Massachusetts Institute of Technology2MIT-IBM Watson AI Lab 3Stanford University4Microsoft yangsl66@mit.edu Abstract The attention mechanism is a core primitive in modern large language models (LLMs) and AI more broadly. Since attention by itself is permutation-invariant, position encoding is essential for modeling structured domains such as language. Rotary position encoding (RoPE) has emerged as the de facto standard approach for position encoding and is part of many modern LLMs. However, in RoPE the key/query transformation between two elements in a sequence is only a function of their relative position and otherwise independent of the actual input. This limits the expressivity of RoPE-based transformers. This paper describes PaTH, a flexible data-dependent position encoding scheme based on accumulated products ofHouseholder(like) transformations, where each transformation is data-dependent, i.e., a function of the input. We derive an efficient parallel algorithm for training through exploiting a compact representation of products of Householder matrices, and implement a FlashAttention-style blockwise algorithm that minimizes I/O cost. Across both targeted synthetic benchmarks and moderate-scale real-world language modeling experiments, we find that PaTH demonstrates superior performance compared to RoPE and other recent baselines. 1 Introduction Attention mechanisms form the backbone of transformer architectures that power contemporary AI systems. Attention is inherently permutation-invariant, and thus encoding positional information into attention is important for effective sequence modeling. Since the original sinusoidal embeddings [ 74], various position encoding schemes have been proposed over the years [ 16,59,25,22,42,55,69,inter alia]; see Dufter et al. [17] for a comprehensive survey. Among these, rotary position embedding [RoPE; 69] has emerged as the de facto standard, adopted in most recent state-of-the-art LLMs. RoPE works by transforming the key ( kj) and query ( qi) embeddings through a rotation matrix Rwhose rotation angle is a function of the difference in positions, resulting in the bilinear form q⊤ iRi−jkjfor the attention logits. The rotation matrix Ritself is a block-diagonal matrix composed of two-by-two rotation matrices, which enables efficient computation. However, the rotation matrix in RoPE is data-independent and only a function of the relative position (i.e., Rapplied i−jtimes), which limits its expressivity; indeed, recent work [ 7] demonstrates that RoPE-based transformers are still computationally constrained to the TC0complexity class, the complexity class of ordinary transformers with absolute position embeddings [ 46]. As a potential consequence, RoPE-based transformers have been empirically found to have difficulty with simple synthetic tasks that require a form of sequential reasoning, such as flip-flop language modeling [ 38] and certain state-tracking tasks [ 48]. Insofar as such simple sequential reasoning underlie real-world capabilities that we want The implementation of the PaTH attention layer is also made available as part of the FLASH LINEAR ATTENTION library [78, 77]: https://github.com/fla-org/flash-linear-attention Preprint. in our LLMs, these failure modes highlight the need to design new primitives that can overcome these theoretical and empirical limitations of existing attention layers. This paper develops PaTH, a position encoding scheme with accumulated Householder transformations, targeting the above problem. In PaTH, the attention logit is still parameterized | https://arxiv.org/abs/2505.16381v1 |
as a bilinear form q⊤ iHijkj, but the matrix Hij∈Rd×dis obtained via a cumulative product ofdata-dependent matrices along the path between positions jandi, where the matrices have Householder-like identity-plus-rank-one structure. Intuitively, this formulation captures the cumula- tive transformation between positions, enabling PaTH to dynamically adapt to input data and solve certain state-tracking problems. Indeed, we show that a constant-layer PaTH-based transformer can solve an NC1-complete problem under AC0reductions, i.e., PaTH can extend transformers beyond theTC0complexity class (assuming TC0̸=NC1). To scale up PaTH Attention, we develop a FlashAttention-like algorithm [ 14] for hardware-efficient parallel training that exploits a compact representation for products of Householder matrices [ 5,24]. Empirical experiments demonstrate that PaTH-based can solve challenging synthetic state-tracking tasks where RoPE-based transformers struggle. On moderate-scale language modeling with 760M- parameter transformers, we show that PaTH can improve upon baselines such as RoPE as well as the Forgetting Transformer [ 36], a recent baseline which modulates the attention logits via a data- dependent additive term. We find that combining the two approaches results in further improvements, and models trained this way generalize well beyond the training sequence length. 2 PaTH Attention PaTH employs a dynamic data-dependent transition matrix for computing the bilinear attention logits, unlike RoPE which applies a fixed transformation at each time step. PaTH adapts its state transitions based on input data by using identity-plus-rank-one Householder-like transformations. 2.1 Generalizing RoPE with Multiplicative Position Encodings Traditional additive position encodings, such as sinusoidal embeddings [ 74] or ALiBi [ 55], represent positions as vectors or matrices summed directly with token embeddings or attention logits. RoPE instead encodes relative positions multiplicatively rather than additively by directly modulating the key/query vectors via position-dependent transformations. The class of multiplicative positional encodings can more generally be defined as Aijsuch that, Aij∝exp k⊤ jiY s=j+1Hs qi , where iandjare positions of the query and key, and Hs∈Rd×dis atransition matrix . RoPE is thus a special case of the above with a static transition matrix Hs=R, where Ris a block diagonal with d/2independent 2-dimensional rotation blocks, each of which has different rotation angles. This static rotation structure allows for efficient computation of RoPE-based attention in practice. 2.2 Data-dependent Multiplicative Position Encodings with PaTH PaTH employs a data-dependent Householder-like1matrix with identity-plus rank-one-structure: Ht=I−βtwtwT t, where wt∈Rdandβt= 2×sigmoid( u⊤xt+b)∈(0,2)are functions of the current input xt.2 We motivate this parameterization from the perspective of generalizing expressive linear RNNs. Concretely, consider linear attention transformers with matrix-valued hidden states St∈Rd×dwith the above Householder-like transition function, where the output ( ot) given the key ( kt), query ( qt), value ( vt) vectors is given by St=St−1Ht+vtk⊤ t, ot=Stqt. 1Householder matrices take the form I−2 ∥u∥2uu⊤and hence our matrix is only Householder-like. 2We use βt∈(0,2)as this allows for negative eigenvalues in the transition matrix [ 20], which has been shown to boost the state tracking performance in the DeltaNet case [ 20,67]. The vector wtis obtained by applying a low-rank linear layer followed by a short convolution layer (filter size 3) and an L2normalization layer. Hence PaTH only adds a marginal number of additional parameters. 2 Recent | https://arxiv.org/abs/2505.16381v1 |
works have shown that such linear RNNs empirically achieve good performance on language modeling [ 62,76,80]. And despite being more efficient than softmax attention, these models have been shown to be (in a certain way) more expressive than transformers [ 20,67], in particular being able to solve a class of state tracking problems that cannot be solved by ordinary transformers. Now consider unrolling the recurrence in the RNN, and compare it against the PaTH-attention output, RNN: ot=tX j=1vj k⊤ j tY s=j+1Hs qt ,PaTH: ot=1 ZttX j=1vjexp k⊤ j tY s=j+1Hs qt , where Zt=Pt j=1exp k⊤ jQt s=j+1Hs qt is the normalizer. This view shows that PaTH is closely related to such expressive linear RNNs, and we thus expect PaTH-based transformers to inherit their increased expressivity. Indeed, the following theorem shows that PaTH can extend transformers beyond the TC0complexity class. Theorem 2.1. A one-layer PaTH transformer with two attention heads and lognprecision can solve anNC1-complete problem under AC0-reductions. The proof, given in appendix A, is a straightforward adaptation of Theorem 2 from Peng et al. [53], which showed the that linear RNNs with a certain kind of data-dependent transition matrix can similarly solve an NC1-complete problem. However, such RNNs still have theoretical limitations that attention does not have, for example in its (in)ability to perform associative recall over a given context of arbitrary length [ 2]. In contrast, PaTH can capture the benefits of both softmax attention (associative recall) and expressive linear RNNs (state tracking). Extension: PaTH-FoX. PaTH simply provides a more expressive way to encode unnormalized attention logits and is thus compatible with other recently proposed modifications to softmax attention such as Stick-Breaking Attention [ 70], Selective Attention [ 33], and Forgetting Transformer [FoX; 36]. As a case study we experiment with combining PaTH with FoX, which additively modifies the attention logits in a data-dependent manner. We show that this combined strategy leads to improved performance on some downstream tasks, especially in length extrapolation. Concretely, FoX [36] modifies the attention via data-dependent “forget” gates fs∈(0,1) Aij∝exp k⊤ jqi+iX s=j+1logfs = iY s=j+1fs exp k⊤ jqi , where fs= sigmoid( u⊤ fxs+bf). Similar to how PaTH can be seen as a softmax version of DeltaNet- style linear RNNs [ 61,79], FoX can be seen as softmax version of GLA-/Mamba2-style linear RNNs [78, 13].3We can combine the two mechanisms to arrive at PaTH-FoX attention: Aij∝ iY s=j+1fs exp k⊤ j iY s=j+1Hs qi . We found this variant to be quite effective on language modeling, reminiscent of the improvements observed by combining DeltaNet with Mamba2 [Gated DeltaNet; 80] in the linear attention case. 3 Efficient Training and Inference for PaTH Attention Efficient kernels for attention [ 14,12,64] work by operating on subblocks of query and key matrices to avoid materialization of the full attention matrix in slower DRAM. Unlike in RoPE however, the cumulative productsQ sHsin PaTH are a function of the input and thus it is not clear whether PaTH- attention computations can similarly be decomposed into computations over subblocks. We now 3However, this analogy is not quite as crisp in | https://arxiv.org/abs/2505.16381v1 |
the Mamba2-FoX case. Mamba2 uses the recurrence St=ftSt−1+vtk⊤ t, and unrolling this would give ot=Pt j=1vjQt s=j+1fs k⊤ jqt. Applying softmax on this would give ot=1 ZtPt j=1vjexpQt s=j+1fs k⊤ jqt , which is different from FoX where the Qt s=j+1fsterm is outside the exponential function. In preliminary experiments we found this softmax version of Mamba2 to greatly underperform FoX. 3 describe how the cumulative product of Householder4transformations can be efficiently computed using a compact representation of Householder products [ 24] and applied in a blockwise fashion [73,44,45,79] to derive a FlashAttention-like algorithm that integrates blockwise Householder transformations with blockwise attention computations. 3.1 Background & Notation We denote the block size along the sequence length dimension as Band define subblocks using the notation A[i],[j]:=AiB:(i+1)B,jB :(j+1)B∈RB×B. This notation extends analogously to the other blocks X[i]:=XiB:(i+1)B,:∈RB×dforX∈ {Q,K,V,W,O}, where (for example) W[i] is obtained from the vectors wiB, . . . ,w(i+1)Bin the Householder transformations. FlashAttention. FlashAttention uses the online softmax trick [ 49,57] to compute the output matrix Oblock by block. Concretely, for each query block iit sequentially process the key/value blocks j from 0toi, computing and accumulating the output as follows: A[i],[j]∝ exp Q⊤ [i]K[j] , ifi < j exp lower( Q⊤ [i]K[i]) ,ifi=j∈RB×B,O[i]=iX j=0A[i],[j]V[j]∈RB×d. The attention submatrices A[i],[j]are computed and processed entirely within SRAM, eliminating the need to write them to slower DRAM, which greatly reduces I/O costs and results in wallclock- speedups. Our algorithm also performs computations of the output block by block, but takes into account the additional contributions from the data-dependent Householder transformations. UT Transform for Products of Householder-like Matrices. A major challenge in computing PaTH attention lies in handling products of Householder-like matrices. We adopt the UT transform [24] to address this efficiently. For a sequence of Ltransformations Ht=I−βtwtw⊤ t, their product can be compactly expressed as: P:=L−1Y t=0Ht=I−W⊤T−1W ∈Rd×d, where T−1:= I+ strictLower( WDW⊤)−1D ∈RL×L. Here, W= [w0, . . . ,wL−1]⊤∈RL×d.D= diag([ β0, . . . , β L−1])∈RL×L. We abuse the notation T−1here for incorporating Dto avoid notational clutter. The UT representation is efficient on modern hardware due to its use of triangular solves and matrix products [ 73], and is often preferred over alternatives such as the WY transform [5, 63]. 3.2 Full Matrix Form of PaTH Attention Recall that in PaTH attention, the attention score is given by Aij∝exp k⊤ jQi t=j+1Ht qi , which involves a cumulative product over arbitrary intervals [j+ 1, i]. A naïve implementation would require recomputing the UT transform for each such interval, which is computationally intractable. However, we show that it is possible to reuse the global matrix inverse T−1and apply simple masking to efficiently extract the product over any subinterval. To represent the product over an intervalQe0 t=s0Ht(with start index s0and end index e0), we use the masked UT transform : e0Y t=s0Ht=I−(W⊙ML s0)⊤T−1(W⊙MR e0), where ⊙denotes element-wise multiplication. The binary masks ML s0,MR e0∈RL×dare defined entrywise as: (ML s0)k,c=1ifk≥s0, 0otherwise ,(MR e0)k,c=1ifk≤e0, 0otherwise . 4We hereon abuse terminology and use “Householder” to refer to our Householder-like transformations. 4 This masked formulation is key to | https://arxiv.org/abs/2505.16381v1 |
deriving the matrix form of PaTH attention: eAij=k⊤ j iY t=j+1Ht qi =k⊤ jqi−k⊤ j(W⊙ML j+1)⊤T−1(W⊙MR i)qi (scalar form) eA= lower( QK⊤)−lower( QW⊤)T−1strictLower( WK⊤) (matrix form) This decomposition enables efficient pairwise attention computation using shared UT structure and interval-specific masking. However, computing the global inverse T−1incurs a prohibitive O(L3)time complexity with respect to sequence length L. In the following section, we introduce a blockwise algorithm that obtain the same result using only local inversions, thereby reducing the overall complexity to match that of standard attention mechanisms. 3.3 Efficient Training To enable hardware-efficient (blockwise) training, cumulative Householder transformations must be pre-applied to the left and right boundaries of each block; otherwise, the token-specific nature of these transformations would render blockwise computation infeasible. To this end, we define boundary-adjusted query and key matrices as follows: (← −Q[i])t= iB+tY m=iB+1Hm! qiB+t=qiB+t−W⊤ [i]T−1 [i](W[i]⊙MR t)qiB+t ∈Rd, (− →K[i])s= (i+1)BY m=iB+s+1Hm ⊤ kiB+s=kiB+s−(T−1 [j]W[j])⊤(W[j]⊙ML s)kiB+s∈Rd, following the derivation in §3.2. In matrix form, these can be expressed as: ← −Q[i]=Q[i]−lower( Q[i]W⊤ [i])T−1 [i]W[i] ∈RB×d, − →K[i]=K[i]− T−1 [i]strictLower( W[i]K⊤ [i])⊤ W[i] ∈RB×d. With these quantities, we express the attention block computation as: A[i],[j]∝ exp← −Q[i]Qi−1 m=j+1P[m]⊤− →K⊤ [j] , ifi > j, exp Q[i]K⊤ [i]−lower( Q[i]W⊤ [i])T−1 [i]strictLower( W[i]K⊤ [i]) ,ifi=j,∈RB×B, where P[i]:=QB j=1HiB+j=W⊤ [i]T−1 [i]W[i]∈Rd×d. Due to associativity, the cross-block term can be computed incrementally :← −Q[i]Qi−1 m=j+1P[m]⊤− →K[j]= (((← −Q[i]P⊤ [i−1])···)P⊤ [j+1])− →K[j]. We adapt the FlashAttention-style block processing framework to perform a right-to-left scan over key/value blocks, enabling this product accumulation in a streaming manner. Concretely the modified blockwise workflow for processing query block iis as follows:5 5Different query blocks can be executed in parallel, following a context-parallel strategy similar to that of FlashAttention-2 [12]. 5 • Load← −Q[i]into SRAM. • For key/value blocks j=i−1, . . . , 0(right-to-left scan): –Load− →K[j],V[j], and P[j]from HBM into SRAM. –Compute logits: eA[i],[j]=← −Q[i]− →K⊤ [j]. –Update online softmax statistics and accumulate output as in FlashAttention. –Update query:← −Q[i]←← −Q[i]P⊤ [j]. • Normalize and store the output to HBM as in FlashAttention. This design preserves the I/O efficiency of FlashAttention while incorporating PaTH’s dynamic positional encoding via streaming cumulative products. Complexity analyses. For each head, the attention computation between a pair of query and key blocks takes O(B2d+Bd2)time-O(B2d)for computing attention scores and O(Bd2)for applying the transition on queries. Since there are (L/B)2such block pairs, the total attention cost is O(L2d+Ld2/B). For preprocessing, computing the local Householder-based transformation for each query/key block involves an inversion step with cost O(B3+B2d). With L/B such blocks, the total preprocessing cost is O(LB2+LBd). When B≈d(which is often the case), the overall complexity is comparable to standard attention, with quadratic scaling in sequence length. 2K 4K 8K 16K Sequence Length0100200300400500600700Execution Time (ms) PaTH-triton FlashAttention-triton FoX-triton Figure 1: Speed comparison be- tween attention variants.Speed Comparison. We implement the PaTH attention kernel6 in Triton [ 72] and benchmark its runtime on a single H100 GPU against FoX and standard RoPE attention under identical settings: batch size 32, 32 heads, head dimension 64, and varying sequence lengths. Results are shown in Figure 1. PaTH | https://arxiv.org/abs/2505.16381v1 |
incurs a modest slowdown compared to RoPE, but outperforms FoX. Further speedups are expected from future kernel-level optimizations (e.g., via ThunderKittens [68]). 3.4 Efficient Inference We can efficiently update historical keys in-place using the current timestep’s transition matrix: k(t) i←(I−βtwtw⊤ t)k(t−1) i for all i < t, (1) where k(i) i=ki. This in-place update strategy eliminates the need to store a separate cache for {wi}i≤tor recompute the somewhat expensive cumulative Householder transformations. Then, the decoding stage becomes equivalent to standard softmax attention decoding, enabling compatibility with existing inference kernels such as FlashDecoding [ 15] and PagedAttention [ 32]. This approach maintains inference efficiency while preserving PaTH’s dynamic positional encoding capabilities. Similarly, PaTH-FoX can be reduced to FoX decoding and thus compatible with the acceleration techniques of FoX (e.g., adaptive pruning [37]). Before decoding, the initial key representations k(i) imust be transformed to k(l) ito account for subsequent Householder transformations. This transformation could be computed blockwise as: K(l) [t]=− − →K[t]P[t+1]···P[⌈l/B⌉]. (2) It is also possible to reuse the suffix cumulative product P[t+1]···P[⌈l/B⌉]across blocks to reduce the overall complexity to linear. 6https://github.com/fla-org/flash-linear-attention/tree/main/fla/ops/path_attn 6 3.5 Discussion Compatibility with context-parallelism (CP) techniques. To extend our FlashAttention2-style context-parallel strategy to distributed settings such as Ring Attention [ 40,35], PaTH’s cumulative Householder transformations must be aligned with the ring-based key/value (KV) passing mechanism. Each device first precomputes its locally transformed queries (← −Q) and keys (− →K) by applying its resident Householder transformations. This also yields the local Householder product matrix P(d) and softmax statistics for its sequence chunk. During inter-device communication, each device transmits its transformed− →Kvectors (with V) and the associated P(d)to the next device in the ring. Upon receiving a (− →K,V,P(d))tuple from an earlier segment, the query-holding device first computes attention outputs using its current← −Qand the incoming (transformed) keys, accumulating both the output and the corresponding online softmax statistics like standard attention. It then updates its← −Qin-place via← −Q←← −Q(P(d))⊤, propagating the cumulative path transformation forward along the ring. This sequence—compute output with current state, then update query state via incoming P(d)—faithfully emulates PaTH’s logical right-to-left scan, enabling correct path reconstruction across distributed segments. Iterative refinement of KV cache. From Eq. 1, it is evident that PaTH iteratively applies a low-rank update to refine the historical key cache, effectively forming a cumulative product of identity-plus- low-rank terms in the attention logit computation. For future work, it would be interesting to (i) extend this update strategy to refine the value vectors, and (ii) more generally design hardware-efficient KV cache refinement schemes that are more expressive than low-rank update as in PaTH. 4 Experiments Method ID OOD Sparse Dense RoPE 6.9% 40.3% 0.01% SBA [70] 9.6% 38.9% 0% FoX [36] 8.3% 36.3% 0% PaTH 0% 0.0001% 0% Table 1: FFLM error rate (%) on ID/OOD test sets. All models are 1- layer, 2-head, 64-dim. 1 2 3 4 N-back0.00.51.0Accuracy MQRAR 5 10 15 20 Sequence Length1234Min. # of Layers A5 FoX PaTHStick-Breaking RoPE Figure 2: Results on MQRAR-N (top) andA5word problem (bottom).We experiment with PaTH attention and compare it against various baselines: ordinary RoPE | https://arxiv.org/abs/2505.16381v1 |
attention, Stick-Breaking Attention (SBA) [ 70], and Forgetting Transformer (FoX) [ 36]. 4.1 Synthetic Tasks Flip-flop language modeling. We first experiment with flip- flop language modeling (FFLM) [ 38], a diagnostic synthetic task which has been found to be challenging for existing ar- chitectures. In this task, the vocabulary consists of Σ = {w,r,i,0,1}. Given a sequence of write-bit ,read-bit , ignore-bit actions, the model must produce the bit ( 0or1) after the most recent write-bit action. For example given the sequence “ w 1 r 1 w 0 i 1 i 0 i 1 r ”, the model is expected to recall the most recently written bit, i.e., 0. De- spite its simplicity, flip-flop language modeling is diagnostic of many real-world capabilities, such as modeling long-range dependencies, the ability to ignore distractors, and sequential reasoning.7Liu et al. [38] find that RoPE-based transformers struggle on this task and provide theoretical insights into why RoPE-based attention mechanisms find it inherently difficult. In Theorem A.1 of the appendix we show that there exists a 2-layer PaTH-based transformer that can solve this task. Empirically, our experiments in Table 1 show that PaTH-based transformers can practically learn to almost perfectly solve this task with only a single layer and two attention heads, including out-of- distribution settings whose frequency of operations are different from than in training (sparse means 98% of the operations are ignore , while dense means only 10% are ignore ). 7The flip-flop monoid induces non-commutative and non-invertible memory dynamics. A constant-depth cascade of parallel flip-flops suffices to simulate all group-free finite-state automata [ 30,81], making it a minimal yet complete primitive for bounded-memory computation and sequential reasoning. 7 Word problems. We showed in §2.2 that PaTH can theoretically extend transformers beyond TC1. However, it is a different question as to whether PaTH transformers can empirically learn to solve NC1-complete problems based on actual data. To test this, we follow Merrill et al. [48] and use a word problem task based on the alternating group A5, a subgroup of S5(on which the canonical NC1-complete word problem is given). This task requires determining if a “word”—a sequence of group operations using fixed generators and their inverses—evaluates to the identity element. Successfully performing this symbolic task means the model must implicitly learn algebraic rules like permutation composition and cancellation. As a concrete example, consider generators g1= (1 2 3) ,g2= (1 2 4) , andg3= (1 2 5) , with their respective inverses g−1 1, g−1 2, g−1 3. Given the word w=g1·g2·g−1 1·g−1 2, the model must determine if wequals the identity permutation. In this instance, wis not the identity, and the model needs to correctly track the sequence of permutations to arrive at this conclusion. Figure 2 (bottom) shows the minimum number of layers required to “solve” this task, where we follow Merrill et al. [48] define “solve” as achieving an accuracy above 90%. For sequences of length 20, PaTH solves it using only 2 layers, whereas other methods require 4 layers, showing PaTH is empirically able to make use of its increased expressivity. | https://arxiv.org/abs/2505.16381v1 |
Multi-query Repeated Associative Recall with N-back (MQRAR- N).We adapt the Multi-query Repeated Associative Recall (MQRAR) task from Tan et al. [70] (itself an enhancement of MQAR [ 1]) to MQRAR- N-back. This task tests a model’s associative recall ability by requiring it to find the N-th last assignment for a given variable, drawing an analogy to the N-back task in experimental psychology [ 27]. Recalling the most recent assignment ( N= 1) can often be accomplished by simpler, recency-focused mechanisms. However, retrieving the N-th last assignment ( N > 1) more rigorously probes a model’s capacity to track an ordered history of states for specific variables, especially when recent information must be ignored. An example sequence for N= 2is: Input A 1 B 2 C 3 D 4 A 5 B 6 A 7 C 8 A 9 B 0 Output ϕ ϕ ϕ ϕ ϕ ϕ ϕ ϕ ϕ ϕ ϕ ϕ 1ϕ ϕ ϕ 5ϕ 2ϕ We compare Transformer models using RoPE, SBA, FoX, and PaTH on their ability to handle MQRAR- N-back with N∈ {1,2,3,4}. All models are 2-layer Transformers with a 256-dimensional hidden state, 2 attention heads. For the task we use 32 key-value pairs a sequence length of 768. Figure 2 shows the results, where we find that PaTH attention can successfully track variable values withN-back recall for N < 4without degradation, whereas recent improvements to attention such as SBA and FoX still struggle on this task. 4.2 Language Modeling We pretrain language models with ∼760M parameters on the Fineweb-Edu corpus [ 51] for 50B tokens using the Mistral tokenizer and a sequence length of 4096. We then evaluate the pretrained models on the following benchmarks. (See the appendix for full details and additional experiments.) Model Wiki. LMB. LMB. PIQA Hella. Wino. ARC-e ARC-c Avg. ppl↓ppl↓acc↑acc↑acc_n↑acc↑ acc↑acc_n↑ ↑ RoPE 19.01 19.77 40.4 70.2 50.3 54.9 67.2 33.3 52.7 FoX 18.33 18.28 41.7 70.8 50.9 57.1 65.7 32.6 53.1 PaTH 18.03 16.79 44.0 70.5 51.5 56.0 68.9 34.4 54.2 PaTH-FoX 17.35 16.23 44.1 70.8 52.2 57.1 67.3 33.9 54.2 Table 2: Results on perplexity and zero-shot commonsense reasoning tasks for 760M models trained on 50B tokens. Best results are highlighted in bold, while the second best results underlined. Standard LM benchmarks. We evaluate on Wikitext perplexity and selected zero-shot common sense reasoning tasks, including of LAMBADA [LMB.; 50] (OpenAI version), PiQA [ 6], Hel- laSwag [Hella.; 82], WinoGrande [Wino.; 60], ARC-easy (ARC-e) and ARC-challenge (Arc-c) [ 10]. Table 2 shows the results. PaTH consistently outperforms RoPE across all tasks, and surpasses FoX on most. PaTH-FoX performs comparably with PaTH while achieving the lower perplexity in both WikiText and LAMBADA. Length extrapolation. Figure 3 presents results on three long-context corpora from different do- mains: PG-19 [ 58] (books), CodeParrot (code), and NarrativeQA [ 28](conversational English). Both 8 PaTH-FoX and FoX generalize well up to 64K tokens,8with PaTH-FoX consistently achieving lower perplexity. The improvement is especially pronounced in the code domain, where state tracking—e.g., tracking variable values—is crucial. PaTH alone generalizes reasonably well, maintaining stable performance up to 32K tokens, | https://arxiv.org/abs/2505.16381v1 |
after which perplexity gradually increases (in contrast to RoPE, which fails abruptly beyond 4K). These results underscore the benefit of data-dependent position encoding and the critical role of the forgetting mechanism in enabling robust generalization to longer contexts. 1K 22K 43K 64K T oken position indices5101520PerplexityCodeParrot 1K 22K 43K 64K T oken position indices1015202530PerplexityPG19 1K 22K 43K 64K T oken position indices1015202530PerplexityNarrative QA FoX PaTH-FoX PaTH RoPE Figure 3: Length extrapolation results for 760M models trained on 50B tokens with 4096 context length. Long-context benchmarks. Table 3 summarizes results on three challenging long-context bench- marks: RULER [ 21], BABILONG [ 31], PhoneBook [ 23], and LongBench-E [ 3]. For RULER, we report the zero-shot average accuracy across all 13 subtasks and also breakdowns by task categories and context length in Figure 4; for BABILONG, we follow standard practice and report the average few-shot accuracy over subproblems QA0–QA5 (see Figure 5 for breakdowns by task and context length); For LongBench-E, we report average scores across three length intervals—0–4K, 4–8K, and 8–16K—and provide detailed results in Table 5. 2K 4K 6K 8K Sequence Length0.00.20.40.60.81.0Accuracy Single-NIAH 2K 4K 6K 8K Sequence Length0.00.20.40.6Accuracy Multi-NIAH 2K 4K 6K 8K Sequence Length0.00.10.20.30.4Accuracy Aggregation 2K 4K 6K 8K Sequence Length0.00.10.20.3Accuracy Variable Tracking FoX PaTH-FoX PaTH RoPE Figure 4: RULER results grouped by different task categories. Model RULER BABILONG PhoneBook LongBench-E 4K 8K 16K 0K 4K 8K 16K 2K 4K 8K 4K 8K 16K RoPE 35.7 1.3 0.0 33.0 13.8 0.0 0.0 32.3 15.6 0.0 18.7 3.7 2.0 FoX 41.6 29.5 4.9 23.8 20.2 8.2 4.4 62.5 38.5 17.7 23.4 16.9 11.7 PaTH 44.6 34.8 18.7 33.8 24.6 16.8 11.6 55.2 20.8 0.0 27.2 22.5 14.4 PaTH-FoX 42.3 34.0 22.6 28.6 25.6 19.2 10.0 89.6 93.8 66.6 23.4 21.8 16.1 Table 3: Summary of average scores on long-context tasks for 760M models with training length 4096. These benchmarks assess different aspects of long-context understanding. Accurate retrieval is critical and is tested by RULER’s Single- and Multi- Needle-In-A-Haystack (NIAH) tasks, as well as by PhoneBook Lookup, an extreme case where every token in the context is a ‘needle”. PaTH-FoX achieves the highest overall retrieval performance, excelling in the more difficult Multi-NIAH and PhoneBook settings. Beyond retrieval, RULER also probes state tracking through its Variable Tracking (VT) task.9 PaTH and PaTH-FoX achieve substantial gains here, consistent with their advantages on synthetic 8FoX’s perplexity somehow increases by several points after around 8K tokens, then stabilizes up to 64K; in contrast PaTH-FoX consistently maintains the lowest perplexity level throughout. 9For example, given “ VAR X1 = 12345, VAR X2 = 3212, ..., VAR X10 = X1, ... ” the query might ask “ Find all variables assigned the value 12345 ”, with the correct answer being “ X1, X10”. 9 state-tracking tasks. BABILONG further tests such capabilities in a narrative setting, embedding bAbI-style logic queries within long PG-19 passages—thus requiring both entity tracking and multi- hop reasoning over extended text. On these tasks as well, PaTH and PaTH-FoX clearly outperform FoX and RoPE. 5 Related Work Data-dependent position encodings. RoPE [ 69] has been | https://arxiv.org/abs/2505.16381v1 |
the de facto position encoding scheme in large language models. However, RoPE’s static nature makes it unsuitable for dynamically adapting to long sequences, motivating works on RoPE length extension [ 52,8,41,inter alia ]. Yet, these methods remain within the RoPE framework and can only mitigate rather solve its limitations. An alternative line of work focuses on data-dependent position encoding. DaPE [ 84] introduces a dynamic attention bias term conditioned on input content, while Forgetting Transformer [ 36] and Cable [ 75] compute this bias via a right-to-left cumulative sum (cumsum), effectively yielding data-dependent variants of ALiBi [ 55]. DaPE-v2 [ 85] further treats the attention map as a 1D feature map and applies a short depthwise Conv1D to promote local interactions among attention logits. This trend of directly manipulating attention logits has gained traction in recent work. Selective Attention [ 33] forms a contextual bias by applying a right-to-left cumsum over attention logits. CoPE [ 19] also computes such a cumsum, but uses it to derive contextualized relative position embeddings [ 65] rather than scalar biases. Stick-Breaking Attention [ 70,66], a unidirectional variant of Geometric Attention [ 11], also accumulates attention logits from right to left. However, instead of using a simple cumulative sum, it adopts a probabilistically principled stick-breaking process via a log-space operator (see [ 70, Algorithm 1]), and computes the final attention scores directly using a sigmoid function. While promising, these approaches operate solely at the attention logit level, modifying the QK⊤ scores through post hoc transformations. However, the dot-product structure is fundamentally lim- ited in its ability to represent more intricate dependencies [ 18,29], motivating work on algebraic position encodings [29], where relative positions are encoded via cumulative matrix products. While conceptually similar to our approach, APE focuses exclusively on data- independent orthogonal (and thus invertible) matrices that are simultaneously diagonalizable [ 56], and thus inherently limited in expressivity [ 9,48,71]. In contrast, our proposed PaTH method addresses this limitation by using data-dependent cumulative Householder-like products, which are non-invertible, non-commutative, and not simultaneously diagonalizable, leading to more expressive transformations of the unnor- malized attention logits. Moreover, PaTH is compatible with other attention variants, such as FoX, providing a principled and extensible framework for positional encoding. Improving state tracking in language models. Transformer-based language models often struggle with state and entity tracking [ 26,54,48]. This is potentially due to the standard transformer architecture’s finding it difficult to reliably emulate finite-state automata [ 38,39,86,4]. To shed light on the theoretical reasons transformers struggle with word problems (tasks requiring careful state tracking), recent studies have analyzed their learning dynamics [ 34] and conducted mechanistic investigations [ 83]. Researchers have also proposed alternative attention mechanisms to enhance self-attention’s expressivity. These aim to capture richer pairwise dependencies than standard dot- product attention, often by incorporating lightweight recurrence—such as right-to-left cumulative sums—into the attention logits [ 19,33,70]. Fagnou et al. [18] propose a matrix-inversion-based attention mechanism for capturing path-level dependencies, which is conceptually similar to our approach. While these methods show empirical improvements in state or entity tracking tasks, they are largely heuristic. In | https://arxiv.org/abs/2505.16381v1 |
this work, we draw inspiration from theoretical studies on parallelizing RNNs while preserving their state tracking capabilities [ 48,20,67,53]. From these, we design a new softmax-based attention mechanism that is performant and efficient. 6 Conclusion This work describes PaTH, a new data-dependent multiplicative position encoding scheme that provably enhances the expressive power of transformers. We develop a FlashAttention-like blockwise algorithm for efficient parallel training. Experiments demonstrate that PaTH consistently outperforms RoPE across multiple benchmarks, especially state tracking tasks and length extrapolation. 10 Acknowledgements This study was supported in part by MIT-IBM Watson AI Lab and the AI2050 program at Schmidt Sciences (Grant G-25-67980). We thank Zhixuan Lin for helpful discussions. References [1]S. Arora, S. Eyuboglu, A. Timalsina, I. Johnson, M. Poli, J. Zou, A. Rudra, and C. Ré. Zoology: Measuring and Improving Recall in Efficient Language Models. CoRR , abs/2312.04927, 2023. [2]S. Arora, S. Eyuboglu, M. Zhang, A. Timalsina, S. Alberti, D. Zinsley, J. Zou, A. Rudra, and C. Ré. Simple linear attention language models balance the recall-throughput tradeoff. CoRR , abs/2402.18668, 2024. doi: 10.48550/ARXIV .2402.18668. URL https://doi.org/ 10.48550/arXiv.2402.18668 . arXiv: 2402.18668. [3]Y . Bai, X. Lv, J. Zhang, H. Lyu, J. Tang, Z. Huang, Z. Du, X. Liu, A. Zeng, L. Hou, Y . Dong, J. Tang, and J. Li. LongBench: A bilingual, multitask benchmark for long context understanding. In L.-W. Ku, A. Martins, and V . Srikumar, editors, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 3119–3137, Bangkok, Thailand, Aug. 2024. Association for Computational Linguistics. doi: 10.18653/v1/ 2024.acl-long.172. URL https://aclanthology.org/2024.acl-long.172/ . [4]S. Bhattamishra, M. Hahn, P. Blunsom, and V . Kanade. Separations in the representational capabilities of transformers and recurrent architectures. In The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024. [5]C. H. Bischof and C. V . Loan. The WY representation for products of householder matrices. InSIAM Conference on Parallel Processing for Scientific Computing , 1985. URL https: //api.semanticscholar.org/CorpusID:36094006 . [6]Y . Bisk, R. Zellers, R. LeBras, J. Gao, and Y . Choi. PIQA: reasoning about physical common- sense in natural language. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020 , pages 7432–7439. AAAI Press, 2020. URL https://aaai.org/ojs/index.php/AAAI/article/view/6239 . [7]B. Chen, X. Li, Y . Liang, J. Long, Z. Shi, and Z. Song. Circuit complexity bounds for rope-based transformer architecture, 2024. URL https://arxiv.org/abs/2411.07602 . [8]S. Chen, S. Wong, L. Chen, and Y . Tian. Extending context window of large language models via positional interpolation, 2023. URL https://arxiv.org/abs/2306.15595 . [9]N. M. Cirone, A. Orvieto, B. Walker, C. Salvi, and T. Lyons. Theoretical foundations of deep selective state-space models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024. URL https://openreview.net/forum?id=3SzrqwupUx . [10] P. Clark, I. Cowhey, O. Etzioni, T. Khot, A. Sabharwal, C. Schoenick, and O. Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge. ArXiv preprint , abs/1803.05457, 2018. URL | https://arxiv.org/abs/2505.16381v1 |
https://arxiv.org/abs/1803.05457 . [11] R. Csordás, K. Irie, and J. Schmidhuber. The neural data router: Adaptive control flow in transformers improves systematic generalization. In International Conference on Learning Representations , 2022. URL https://openreview.net/forum?id=KBQP4A_J1K . [12] T. Dao. Flashattention-2: Faster attention with better parallelism and work partitioning. In The Twelfth International Conference on Learning Representations , 2024. URL https:// openreview.net/forum?id=mZn2Xyh9Ec . [13] T. Dao and A. Gu. Transformers are ssms: Generalized models and efficient algorithms through structured state space duality. arXiv preprint arXiv:2405.21060 , 2024. 11 [14] T. Dao, D. Y . Fu, S. Ermon, A. Rudra, and C. Re. Flashattention: Fast and memory-efficient exact attention with IO-awareness. In A. H. Oh, A. Agarwal, D. Belgrave, and K. Cho, editors, Advances in Neural Information Processing Systems , 2022. URL https://openreview.net/ forum?id=H4DqfPSibmx . [15] T. Dao, D. Haziza, F. Massa, and G. Sizov. Flash-decoding for long-context inference, October 13 2023. URL https://pytorch.org/blog/flash-decoding/ . [16] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technolo- gies, volume 1 (long and short papers) , pages 4171–4186, 2019. [17] P. Dufter, M. Schmitt, and H. Schütze. Position information in transformers: An overview. Computational Linguistics , 48(3):733–763, 2022. [18] E. Fagnou, P. Caillon, B. Delattre, and A. Allauzen. Chain and Causal Attention for Efficient Entity Tracking. In Y . Al-Onaizan, M. Bansal, and Y .-N. Chen, editors, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages 13174–13188, Miami, Florida, USA, Nov. 2024. Association for Computational Linguistics. doi: 10.18653/ v1/2024.emnlp-main.731. URL https://aclanthology.org/2024.emnlp-main.731/ . [19] O. Golovneva, T. Wang, J. Weston, and S. Sukhbaatar. Contextual position encoding: Learning to count what’s important, 2024. URL https://arxiv.org/abs/2405.18719 . [20] R. Grazzi, J. Siems, J. K. Franke, A. Zela, F. Hutter, and M. Pontil. Unlocking state-tracking in linear RNNs through negative eigenvalues. In The Thirteenth International Conference on Learning Representations , 2025. URL https://openreview.net/forum?id=UvTo3tVBk2 . [21] C.-P. Hsieh, S. Sun, S. Kriman, S. Acharya, D. Rekesh, F. Jia, and B. Ginsburg. RULER: What’s the real context size of your long-context language models? In First Conference on Language Modeling , 2024. URL https://openreview.net/forum?id=kIoBbc76Sy . [22] Z. Huang, D. Liang, P. Xu, and B. Xiang. Improve transformer models with better relative position embeddings. arXiv preprint arXiv:2009.13658 , 2020. [23] S. Jelassi, D. Brandfonbrener, S. M. Kakade, and E. Malach. Repeat After Me: Transformers are Better than State Space Models at Copying. CoRR , abs/2402.01032, 2024. doi: 10. 48550/ARXIV .2402.01032. URL https://doi.org/10.48550/arXiv.2402.01032 . arXiv: 2402.01032. [24] T. Joffrain, T. M. Low, E. S. Quintana-Ortí, R. A. van de Geijn, and F. G. V . Zee. Accumulating householder transformations, revisited. ACM Trans. Math. Softw. , 32:169–179, 2006. URL https://api.semanticscholar.org/CorpusID:15723171 . [25] G. Ke, D. He, and T.-Y . Liu. Rethinking positional encoding in language pre-training. arXiv preprint arXiv:2006.15595 , 2020. [26] N. Kim and S. Schuster. Entity Tracking in Language Models. In A. Rogers, J. Boyd- Graber, and N. Okazaki, editors, Proceedings of the 61st | https://arxiv.org/abs/2505.16381v1 |
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