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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 Chicago2T... | 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 tra... | 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 CheXp... | 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... | 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 ... | 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 ta... | 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... | 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 substant... | 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 = incorre... | 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 ex... | 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- fer... | 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 othe... | 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 r... | 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 Linguistic... | 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. Thi... | 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 ... | 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 ... | 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... | 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 ... | 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, rig... | 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 ... | 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 sec... | 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 s... | 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 sug... | 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 i... | 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... | 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 evalu... | 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... | 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 fro... | 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,... | 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... | 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 t... | 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 f... | 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 LE... | 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 n... | 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 0.657... | 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... | 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 correl... | 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 e... | 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. Subsequ... | 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... | 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 incorpor... | 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 Confer... | 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. ... | 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. 4969–49... | 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., Mat... | 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... | 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 s... | 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 highlight... | 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. Resear... | 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 ... | 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 EMENT... | 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 comple... | 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 me... | 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 ... | 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 provi... | 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 t... | 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 detai... | 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... | 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. Mord... | 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. ... | 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. Fo... | 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 . Y... | 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-powere... | 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 ... | 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 ... | 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 ... | 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 ... | 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... | 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 t... | 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 o... | 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... | 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 inc... | 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 ... | 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, w... | 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 m... | 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: Over... | 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... | 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... | 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, nlt... | 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 informa... | 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 evaluati... | 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. M... | 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. S... | 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.2407.014... | 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 Sco... | 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 fo... | 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... | 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.... | 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,... | 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 trac... | 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 experimen... | 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, c... | 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... | 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... | 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 v... | 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 indices5101520... | 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 limit... | 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 posi... | 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-... | https://arxiv.org/abs/2505.16381v1 |
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