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2 12 2 1 3 18 57 113 57 66 4 2 0 3 26 1164 39 1 830 13 8 7 8 5 2 1 77 5 1008 485 54 93 65 24 37 5 11 1 370 1 257 15 2 18 8 2 20 1 407 185 4 237 5 9 9 5 23 0 354 53 0 16 138 1 13 1 29 2 352 121 39 23 0 415 24 4 66 23 291 43 0 1 4 2 432 68 11 4 3 6 0 0 0 0 1 501Janus(y) vs Qwen(x) airplane automobilebirdcatdeerdogfroghor... | https://arxiv.org/abs/2505.16149v1 |
and true labels using the Confident Learning paradigm. Docta [ 57] systematically audits dataset credibility by estimating label noise and ranking instances based on the alignment between observed and inferred labels without requiring ground-truth annotations. For the results obtained from VLMs and related methods, our... | https://arxiv.org/abs/2505.16149v1 |
dataset, likely due to its higher-resolution images, and perform well on ImageNet-1K, where Cleanlab underperforms. Conversely, Cleanlab outperforms VLMs on MNIST, indicating that statistical or human-guided methods are more effective for structured data. These findings suggest a complementary relationship between VLMs... | https://arxiv.org/abs/2505.16149v1 |
also contains “man” and “boy”, suggesting genuine label omissions rather than probabilistic ambiguity. 8 Observation 7: When the number of label candidates is extremely large (e.g., in the case of ImageNet), VLMs tend to produce semantically related sets of labels. In datasets with a large number of possible labels, su... | https://arxiv.org/abs/2505.16149v1 |
missing labels, providing soft-labeled outputs that exhibit a high degree of alignment with human judgments . Our work offers a model- centric alternative for benchmark improvement, and we hope it inspires future efforts in multi-label evaluation, open-vocabulary testing, and human-in-the-loop verification. 10 Referenc... | https://arxiv.org/abs/2505.16149v1 |
Scaling up visual and vision-language representation learning with noisy text supervision. In International conference on machine learning , pages 4904–4916. PMLR, 2021. 11 [17] Evgeny Krivosheev, Siarhei Bykau, Fabio Casati, and Sunil Prabhakar. Detecting and preventing confused labels in crowdsourced data. Proceeding... | https://arxiv.org/abs/2505.16149v1 |
label errors in test sets destabilize machine learning benchmarks. arXiv preprint arXiv:2103.14749 , 2021. 12 [35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural l... | https://arxiv.org/abs/2505.16149v1 |
visual encoding for unified multimodal understanding and generation. arXiv preprint arXiv:2410.13848 , 2024. [50] Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, and Masashi Sugiyama. Are anchor points really indispensable in label-noise learning? Advances in Neural Information Processing Systems ,... | https://arxiv.org/abs/2505.16149v1 |
REVEAL effectively identifies missing labels and provides soft-labeled outputs accompanied by likelihood estimates, facilitating its generalization to diverse classification tasks. Compared to traditional one-hot encoding approaches, soft-labeling offers a more nuanced representation that integrates both linguistic and... | https://arxiv.org/abs/2505.16149v1 |
think step by step and provide similar characteristic in details between the label you choose and the image.All the answer should be in format {‘answer’:‘your answer 1,your answer 2’,‘reason’:‘reason of the answers’} Batched Multi-Label Selection Forget you previous answer. Describe the image and choose labels from the... | https://arxiv.org/abs/2505.16149v1 |
matching scores between each image and all possible class labels (10 for CIFAR-10, 100 for CIFAR-100). The resulting scores are passed through a softmax transformation to yield a probability distribution over the candidate labels. A threshold is then applied to determine which labels are retained. We deploy BLIP-2 loca... | https://arxiv.org/abs/2505.16149v1 |
addresses the root cause by estimating the joint distribution between noisy (observed) and true (latent) labels. This is achieved through three key principles: pruning , to identify and remove noisy labels; counting , to estimate noise rates using calibrated frequency statistics; and ranking , to prioritize training ex... | https://arxiv.org/abs/2505.16149v1 |
we define the Agreement Rate based on four distinct MTurk outcome types. Let Ridenote the set of predicted labels for image iby a given method (e.g., a VLM or REVEAL), and let giandsirepresent the given (i.e., original) and guessed (i.e., Cleanlab-predicted) labels for that image. Agreement is determined under the foll... | https://arxiv.org/abs/2505.16149v1 |
arXiv:2505.16160v3 [cs.CL] 28 May 2025EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios Bin Xu∗, Yu Bai∗, Huashan Sun∗, Yiguan Lin* Siming Liu, Xinyue Liang, Yaolin Li, Yang Gao†, Heyan Huang School of Computer Science and Technology, Beijing Institute ... | https://arxiv.org/abs/2505.16160v3 |
school students, high school students, graduate students, etc.), and different subjects. All the above categories formulate different education contexts with a total number of over 4,000. For these dif- ferent contexts, we create different querying data, resulting in a dataset containing 18,821 data points. In designin... | https://arxiv.org/abs/2505.16160v3 |
achieve comparable performance with powerful state-of-the-art LLMs. 2 Related Work 2.1 Evaluation Benchmarks of LLMs The evolution of evaluation frameworks has been pivotal in benchmarking general LLM ca- pabilities, yet they remain limited in address- 2 Knowledge Distillation Metrics & Evaluation Data Curation 9 scena... | https://arxiv.org/abs/2505.16160v3 |
them valuable tools for instructional design. In parallel, researchers have increasingly em- ployed LLMs as interactive tutors. Such sys- tems guide students through programming con- cepts (Vadaparty et al., 2024; Frankford et al., 2024b), assist with code debugging and explana- tion (MacNeil et al., 2023; Wang et al.,... | https://arxiv.org/abs/2505.16160v3 |
A. 3.2 Educational Context Design To ensure scenario realism, learner alignment, and meaningful evaluation, we construct diverse educa- tional domain contexts that reflect the conditions under which tasks naturally occur. Each context is designed to capture variation across four primary dimensions: subject taxonomy ,ta... | https://arxiv.org/abs/2505.16160v3 |
this process is as follows: Score =1 nnX i=1Evaluator i(x, y,M) where M ∈ { Handcrafted ,Model generated }(1) 4.2 Metric Design We design distinct evaluation metrics based on 3 scenarios, with 4 metrics for each scenario to cover its key aspects, resulting in 12 different metrics.Scenario Adaptation The Scenario Adapta... | https://arxiv.org/abs/2505.16160v3 |
QwQ Plus (Qwen, 2025), GPT-4o (Ope- nAI et al., 2024), DeepSeek R1 (Guo et al., 2025), and DeepSeek V3 (Liu et al., 2024), as evaluators due to their strong scenario understanding, broad knowledge, and accurate intent recognition. These models assess responses using our defined metrics, guided by dedicated prompts that... | https://arxiv.org/abs/2505.16160v3 |
in resource-constrained settings. 6 Evaluator Model BFA CSI CRSC DKA EICP HOTS IFTC MGP PAS RPR RTC SEI Average DeepSeek V3DeepSeek R1 9.51 8.75 9.44 9.45 7.61 8.53 9.47 7.76 9.64 8.85 9.14 9.06 8.93 DeepSeek V3 9.57 8.61 9.25 9.27 7.23 7.98 9.21 7.56 8.94 8.76 9.00 8.59 8.66 Qwen Max 9.38 8.53 9.12 9.23 7.43 7.99 9.16... | https://arxiv.org/abs/2505.16160v3 |
as current evaluators are not reward models. 2) RLHF training makes models reluctant to give negative feedback. However, we believe post-training could mitigate this issue, and we’ll explore this direction in future work. Larger models typically outperform smaller ones across scenarios. Top models like DeepSeek R1 exce... | https://arxiv.org/abs/2505.16160v3 |
results are underlined. For our distillation model, we use Qwen2.5-7B-Instruct as the base model. For simplicity, we use abbreviations for the metrics. Full names of each metric can be found in Table 2. Model DeepSeek R1 GPT-4o QwQ-Plus DeepSeek V3 Human DeepSeek R1 - 0.55 0.61 0.65 0.63 GPT-4o 0.55 - 0.57 0.58 0.56 Qw... | https://arxiv.org/abs/2505.16160v3 |
query data was generated by models, which may not fully reflect realistic or diverse user intent. Fu- ture work could benefit from incorporating more human-written queries. Additionally, while our work explores the correlation between human and model evaluations, there is still room to improve alignment. We also employ... | https://arxiv.org/abs/2505.16160v3 |
the State of the Art of Programming Exercise Generation Using Large Language Models. In 2024 36th International Conference on Software Engineering Education and Training (CSEE&T) , pages 1–5, Würzburg, Ger- many. IEEE. Eduard Frankford, Clemens Sauerwein, Patrick Bass- ner, Stephan Krusche, and Ruth Breu. 2024b. AI... | https://arxiv.org/abs/2505.16160v3 |
Liyanage and Surangika Ranathunga. 2019. A Multi-language Platform for Generating Algebraic Mathematical Word Problems. In 2019 14th Confer- ence on Industrial and Information Systems (ICIIS) , pages 332–337, Kandy, Sri Lanka. IEEE. Evanfiya Logacheva, Arto Hellas, James Prather, Sami Sarsa, and Juho Leinonen. 2024. Ev... | https://arxiv.org/abs/2505.16160v3 |
Araya. 2023. Automati- cally Detecting Incoherent Written Math Answers of Fourth-Graders. Systems , 11(7):353.Annapurna Vadaparty, Daniel Zingaro, David H. Smith, Mounika Padala, Christine Alvarado, Jamie Gorson Benario, and Leo Porter. 2024. CS1-LLM: Integrat- ing LLMs into CS1 Instruction. arXiv preprint . Alex Wang,... | https://arxiv.org/abs/2505.16160v3 |
across various subjects and difficulty levels. •Error Correction: The capacity to identify and correct student errors in assignments, exams, or daily exercises. Errors can range from obvious mistakes to subtle issues such as variable misuse in code or logical flaws in mathematical reasoning. Evaluation focuses on the a... | https://arxiv.org/abs/2505.16160v3 |
demanding critical reasoning and expertise. Language: • EduBench currently supports tasks in Chinese and English . Question Types: The question type dimension captures the format of interaction or evaluation expected from the model: •Standard Scenarios (e.g., Problem Solving, Idea Provision, Grading): –Single Choice –M... | https://arxiv.org/abs/2505.16160v3 |
including tailored suggestions, scaffolded prompts, and relevant resource recommendations. •Higher-Order Thinking & Skill Development: This sub-metric examines whether the response promotes advanced cognitive skills, such as critical thinking, problem-solving, creative reasoning, and the ability to transfer knowledge t... | https://arxiv.org/abs/2505.16160v3 |
Correction E.2, Idea Provision E.3, Personalized Learning Support E.4, Emotional Support E.5, Question Generation E.6, Automatic Grading E.7, Teaching Material Generation E.8, Personalized Content Creation E.9. E.1 Problem Solving Problem Solving Prompt Design Please freely generate an appropriate question based on the... | https://arxiv.org/abs/2505.16160v3 |
a personalized service customization expert, providing tailored services to improve learning efficiency. Please freely generate a specific and appropriate student profile based on the following subject and difficulty level, and provide learning path planning suggestions and personalized recommendations. The question ty... | https://arxiv.org/abs/2505.16160v3 |
question generation: Generate questions for different difficulty levels, question types, and knowledge scopes. - Comprehensive question generation: Cross-reference multiple knowledge points to generate questions. - Additional requirements: - Provide solutions and step-by-step scoring references for questions. - Compile... | https://arxiv.org/abs/2505.16160v3 |
design, etc. "Teaching Material": Return in JSON format. E.9 Personalized Content Creation Personalized Content Creation Prompt Design You are an intelligent assistant capable of generating personalized learning content or tasks based on individual student differences. Please freely generate a student profile for a stu... | https://arxiv.org/abs/2505.16160v3 |
•5-6: Attempts to match the role and tone can be seen, but overall consistency is weak; some expressions are disconnected from the role/scenario. •3-4: Significant mismatch in role and tone; comes across as unnatural or inconsistent. •1-2: No reflection of assigned role/tone; expression entirely inconsistent with the s... | https://arxiv.org/abs/2505.16160v3 |
complete, clear, and rigorous; all steps are correct; arguments are strong and free of logical fallacies. •7-8: Reasoning is largely correct and logically coherent with minor issues in individual steps or details that do not affect the conclusion. •5-6: Reasoning is visible but contains unclear logic, missing steps, or... | https://arxiv.org/abs/2505.16160v3 |
value. •3-4: Little to no personalization; output is the same for everyone; learning support is insufficient or unrelated. •1-2: No personalization; output may conflict with student needs; offers no or incorrect learning support. 25 F.3.4 Higher-Order Thinking & Skill Development (HOTS) Description: Does the interactio... | https://arxiv.org/abs/2505.16160v3 |
the exact win rates. Notably, none of the evaluators exhibit a strong 26 Model Basic Factual Accuracy Domain Knowledge Accurac DeepSeek R1 GPT-4o QwQ-Plus DeepSeek V3 Human DeepSeek R1 GPT-4o QwQ-Plus DeepSeek V3 Human DeepSeek R1 - 0.51 0.66 0.68 0.59 - 0.6 0.59 0.58 0.57 GPT-4o 0.51 - 0.57 0.56 0.59 0.6 - 0.59 0.62 0... | https://arxiv.org/abs/2505.16160v3 |
whereas the general-purpose model demonstrates a more balanced evaluation. Notably, the reasoning model’s bias is substantial, with a win ratio as skewed as 9 to 1 in favor of reasoning models. These results highlight the importance of incorporating both reasoning and normal evaluators in the assessment process to miti... | https://arxiv.org/abs/2505.16160v3 |
data selection strategies. The first strategy involves selecting the best generation model within each scenario. The specific process includes calculating the average scores of all evaluation metrics 28 Scenario Category Dimensions All Data For Training Chinese English Total Chinese English Total Problem Solving Durati... | https://arxiv.org/abs/2505.16160v3 |
model in each scenario from all distilled data. The second strategy focuses on selecting optimal models for each evaluation metric. We calculate the average score of each generation model on individual metrics, rank them accordingly to identify the best model for each metric. During the final distilled data screening p... | https://arxiv.org/abs/2505.16160v3 |
KNN-SSD : Enabling Dynamic Self-Speculative Decoding via Nearest Neighbor Layer Set Optimization Mingbo Song1, Heming Xia2, Jun Zhang3, Chak Tou Leong2, Qiancheng Xu2,Wenjie Li2,Sujian Li1 1National Key Laboratory for Multimedia Information Processing, Peking University 2Department of Computing, The Hong Kong Polytechn... | https://arxiv.org/abs/2505.16162v1 |
construct a compact draft model. In this work, we find that the selection of skipped layers is not universal . Instead, one skip- 1arXiv:2505.16162v1 [cs.CL] 22 May 2025 layer configuration could be sensitive to domain shifts. For example, when applying a configuration derived from the summarization task to other tasks... | https://arxiv.org/abs/2505.16162v1 |
layers. However, both approaches are trained on a single data type and struggle with di- verse data streams. Our work aims to tackle this problem by integrating samples from various do- mains. Sparsity and Model Compression. Sparsity and model compression are essential for enhancing the efficiency of LLMs by reducing a... | https://arxiv.org/abs/2505.16162v1 |
types are unpredictable. To achieve both high inference efficiency and minimal performance degradation, task-specific configurations are essen- tial. This motivates the development of KNN-SSD ,which dynamically selects the most suitable skip- layer configuration based on task characteristics, ensuring robust and effici... | https://arxiv.org/abs/2505.16162v1 |
to fit a KNN model. When a new sample is input, KNN-SSD first uses its last hidden vector as the input representative and queries the KNN model. Based on the retrieved result, it selects the corresponding skip layer set to perform decoding, thereby achieving acceleration. thej-th layer should be skipped (z(j) i= 1) or ... | https://arxiv.org/abs/2505.16162v1 |
1.25× 13.49 1.25 × KNN-SSD 1.52× 1.49× 1.50× 1.52× 16.30 1.51 × Table 1: Comparison between KNN-SSD and two Self-SD methods. R indicates the mix ratio of sample streams. We report the expected speedup ratio under different mix ratios, average decoding speed (token/s) under greedy decoding, and average speedup ratio amo... | https://arxiv.org/abs/2505.16162v1 |
searched configuration was subsequently applied for inference and also remained unchanged, which is denoted as Self-SD(Mix). Evaluation Metrics. We evaluate KNN-SSD using two standard metrics commonly adopted in evalu- ation: the mean generated length M(Stern et al., 2018) and the token acceptance rate α(Leviathan et a... | https://arxiv.org/abs/2505.16162v1 |
than the other two Self-SD methods. Out of Domain Generalization. We adopt the XSUM (Narayan et al., 2018) dataset, the MATH (Hendrycks et al., 2021) dataset, and the Alpaca (Taori et al., 2023) dataset as out-of-domain tasks to assess KNN-SSD ’s generalizability. XSUM and CNN/DM datasets belong to summarization tasks,... | https://arxiv.org/abs/2505.16162v1 |
how our method works, we provide a case study that presents a typ- ical sample stream. In Figure 8, a sample stream contains three common types of queries a user might ask: summarization, reasoning, and trans- lation. For each input query, KNN-SSD will first compute its last hidden vector and then use a KNN model to fi... | https://arxiv.org/abs/2505.16162v1 |
KNN-SSD achieves a notable speedup on various models. First, we did not incorporate draft tree ver- ification, which has been shown to improve the to- ken acceptance rate (Xia et al., 2025). Second, our current evaluation is limited to models of moderate scale. Due to practical considerations related to computational r... | https://arxiv.org/abs/2505.16162v1 |
and Dan Alistarh. 2023. SparseGPT: Mas- sive language models can be accurately pruned in one-shot. In Proceedings of the 40th International Conference on Machine Learning , volume 202 of Proceedings of Machine Learning Research , pages 10323–10337. PMLR. Yichao Fu, Peter Bailis, Ion Stoica, and Hao Zhang. 2024. Break t... | https://arxiv.org/abs/2505.16162v1 |
Empirical Methods in Natural Lan- guage Processing , Brussels, Belgium. OpenAI, Josh Achiam, Steven Adler, Sandhini Agar- wal, Lama Ahmad, Ilge Akkaya, et al. 2024. Gpt-4 technical report. Preprint , arXiv:2303.08774. Gunho Park, Baeseong Park, Minsub Kim, Sungjae Lee, Jeonghoon Kim, Beomseok Kwon, Se Jung Kwon, Byeong... | https://arxiv.org/abs/2505.16162v1 |
and Ruiwen Xu. 2025. Learning harmonized rep- resentations for speculative sampling. Preprint , arXiv:2408.15766. 11 A Preliminary Details We visualize the optimal skipped layer sets we searched across five tasks on two series of models in Figure 9 and Figure 10. B Datasets We mainly evaluate KNN-SSD on LLaMA-2 (Tou- v... | https://arxiv.org/abs/2505.16162v1 |
the self-instruct method, based on the outputs of a strong language model. It covers a wide range of tasks, making it suitable for us to test the gener- alizability of KNN-SSD . C Experimental Details C.1 Setups During the pre-inference stage, we set the maxi- mum iterations of Bayesian Optimization to 1,000 and the nu... | https://arxiv.org/abs/2505.16162v1 |
66 68 70 72 74 76 78 80 ATT 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 (e) Text-to-SQL - Spider2 Figure 9: Visualization of skipped layer set configuration of LLaMA-2-13B optimized by Self-SD (Zhang et al., 2024) on different task domains. Gray squ... | https://arxiv.org/abs/2505.16162v1 |
62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 ATT 1 3 5 7 911 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 (c) Translation - WMT16 MLP 2 4 6 810 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68... | https://arxiv.org/abs/2505.16162v1 |
arXiv:2505.16164v1 [cs.CL] 22 May 2025Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task Mengyang Qiu1*Zoe Brisebois1Siena Sun2 1Department of Psychology, Trent University, Canada 2Speech-Language Pathology Program, Saint Elizabeth University, United States {mengyangqiu, zoebriseb... | https://arxiv.org/abs/2505.16164v1 |
human participants. It is worth noting that Wang et al. (2025) rec- ognized this limitation and attempted to address it by prompting LLMs to role-play 30 different oc- cupations, effectively simulating multiple distinct agents. However, the resulting semantic networks still failed to match the flexibility and associati... | https://arxiv.org/abs/2505.16164v1 |
explore whether ensembling can simulate the distributional variability seen in human behavior. In preview, our findings suggest a clear answer: LLMs are highly capable of producing fluent and correct answers in the phonemic fluency task, but none of them match the full scope of human be- havioral variability. The remai... | https://arxiv.org/abs/2505.16164v1 |
the participant age, education, and number of correct responses. We also tested three reduced-information variants: a demographic-only prompt (no number of cor- rect responses), a performance-only prompt (no 2https://cloud.google.com/speech-to-text 3https://openai.com/api/ 4https://www.anthropic.com/api 5https://ai.goo... | https://arxiv.org/abs/2505.16164v1 |
" Here is their demographic and performance information :\n" f" Age : { age }\n" f" Highest degree : { education }\n" f" Number of correct responses : { num_correct }\n\n" "Now , please imagine that you are this participant . Your task is to generate as many words as possible that begin with the letter F, as if you wer... | https://arxiv.org/abs/2505.16164v1 |
only one participant) relative to the total number of types. This provides insight into how consistent or individualized the generated words are across participants within the group (Castro et al., 2021). As shown in Table 2, human participants pro- duced the highest number of unique types (476) and idiosyncratic types... | https://arxiv.org/abs/2505.16164v1 |
Models (Word Types)– 225 75 1778 0.13 0.33 100+ Word Types (9 models)– 180 46 1785 0.10 0.26 All-Model Mix (17 models)– 169 45 1780 0.09 0.27 4 Item-Level Analysis 4.1 Distribution of production frequency Previous studies have observed that word produc- tion in verbal fluency tasks typically follows Zipf’s law, a type ... | https://arxiv.org/abs/2505.16164v1 |
with frequency of responses in human data. Word frequency showed the strongest positive correlation ( r= 0.55), indicating that high-frequency words were more likely to be pro- duced. Word length and age of acquisition were negatively correlated with production frequency (r=−0.39andr=−0.52, respectively), suggest- ing ... | https://arxiv.org/abs/2505.16164v1 |
count, variability, production frequency distribu- tion, and linguistic predictors of word choice. In contrast, O3-mini and the ensemble models showed substantially divergent patterns with lower explained variance and different predictor pro- files, suggesting fundamentally different underly- ing mechanisms driving the... | https://arxiv.org/abs/2505.16164v1 |
tighter local clusters with weaker global integra- tion, whereas Claude responses form a more evenly connected network with greater global efficiency. To complement the network analysis, we con- ducted a representational similarity analysis (RSA; Nili et al., 2014) comparing the original pairwise similarity matrices th... | https://arxiv.org/abs/2505.16164v1 |
as sig- nificant predictors across systems. However, impor- tant differences were also evident. For human par- ticipants, orthographic neighborhood size played a unique and substantial role, with words having more neighbors being produced more frequently.In contrast, this factor was less influential in LLM outputs, whi... | https://arxiv.org/abs/2505.16164v1 |
Keith A Hutchison, Michael J Cortese, Brett Kessler, Bjorn Loftis, James H Neely, Douglas L Nelson, Greg B Simpson, and Rebecca Treiman. 2007. The English lexicon project. Behavior Research Methods , 39(3):445–459. Katy Borodkin, Yoed N Kenett, Miriam Faust, and Nira Mashal. 2016. When pumpkin is closer to onion than t... | https://arxiv.org/abs/2505.16164v1 |
2020. A large-scale semantic analysis of verbal flu- ency across the aging spectrum: Data from the Cana- dian Longitudinal Study on Aging. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences , 75(9):e221–e230. Sean Trott. 2024. Large language models and the wis- dom of small crowds. Open Mi... | https://arxiv.org/abs/2505.16164v1 |
similarly retained only age of acquisition [ F(1,162) = 52 .04, p < . 001], explaining 24.3%of variance (adjusted R2=.238, β=−.49, t=−7.21, p < . 001). 10 Table A1: Overview of LLMs prompted in the letter Ffluency task Provider Model Temperature Prompt Type OpenAI (15 model configurations) gpt-4.1-2025-04-14 0.3, 0.7 (... | https://arxiv.org/abs/2505.16164v1 |
arXiv:2505.16170v2 [cs.CL] 27 May 2025When Do LLMs Admit Their Mistakes? Understanding the Role of Model Belief in Retraction Yuqing Yang University of Southern California yyang063@usc.eduRobin Jia University of Southern California robinjia@usc.edu Abstract Can large language models (LLMs) admit their mistakes when the... | https://arxiv.org/abs/2505.16170v2 |
answers that they know are incorrect. Given the lack of a suitable testbed for studying retraction, we first construct model-dependent “continuation” datasets. We obtain two datasets of knowledge-based questions on which LLMs often hallucinate: a set of constraint satisfaction questions where each question includes two... | https://arxiv.org/abs/2505.16170v2 |
and its external retraction behavior, and identify the underlying mechanism that governs this behavior. (3) We demonstrate that the causal influence of internal belief on retraction generalizes to supervised fine-tuned models, where more accurate beliefs lead to improved retraction performance. 2 Related Work 2.1 Probi... | https://arxiv.org/abs/2505.16170v2 |
questions, we construct model- specific “continuation” datasets. Each example pairs a question with a model-generated answer, and we prompt the model to continue generating to evaluate whether it will retract, as illustrated below: USER: Name a politician who was born in New York City. ASSISTANT: Hillary Clinton [Model... | https://arxiv.org/abs/2505.16170v2 |
incorrect. Thus, it appears the model has both the knowledge and the ability to retract. Then, why do LLMs fail to retract more incorrect answers? What factors govern their retraction behavior? 4 Model Belief Guides Retraction To understand why LLMs are often unwilling to retract their own incorrect answers, we first i... | https://arxiv.org/abs/2505.16170v2 |
scores to CN and WN examples, and low scores to CR and WR examples. This indicates that the model’s beliefs do not align with factual correctness, but instead align more closely with its retraction behavior: low belief scores correspond to retraction, while high scores correspond to non-retraction. For example, WN exam... | https://arxiv.org/abs/2505.16170v2 |
the optimal steering direction. Instead, we aim to understand when and why LLMs choose to retract. Both the probing and steering results support the conclusion that the model’s belief—defined independently of retraction and trained on separate data—causally affects retraction behavior and generalizes across different d... | https://arxiv.org/abs/2505.16170v2 |
retraction by modulating attention to the given answer, we calculate the attention weights from the last token of the answer to the answer span. Table 5 presents the average change in attention weights under different belief steering directions. Consistent with our hypothesis, negative belief steering increases the mod... | https://arxiv.org/abs/2505.16170v2 |
the internal repre- sentation of the answer, in addition to affecting next token prediction. In Table 7, we also present patching results for Llama3.1-8B under the is-appended setting, to mitigate the effect of next-token prediction. When this influence is reduced, attention value vectors play a more prominent role. Th... | https://arxiv.org/abs/2505.16170v2 |
Layer0.20.30.40.50.60.70.8Avg. Probe Score Base-C Base-W SFT-C SFT-W (b) Llama3.1-8B on C ELEBRITY . Figure 4: Average probe scores across layers for Llama3.1-8B (Base) and its fine-tuned variant (SFT). “C” denotes correct examples, and “W” denotes wrong examples. Finally, we probe the model’s internal beliefs after su... | https://arxiv.org/abs/2505.16170v2 |
Jingbo Shang. Can llms learn from previous mistakes? investigating llms’ errors to boost for reasoning. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar, editors, Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2024, Bangkok, Thailand, August 11-16, 2... | https://arxiv.org/abs/2505.16170v2 |
, 2023. URL http://papers.nips.cc/paper_files/paper/2023/hash/ 81b8390039b7302c909cb769f8b6cd93-Abstract-Conference.html . Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wan... | https://arxiv.org/abs/2505.16170v2 |
Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023. URL https://openreview.net/forum?id=ETKGuby0hcs . Sky CH-Wang, Benjamin Van Durme, ... | https://arxiv.org/abs/2505.16170v2 |
Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, and Jared Kaplan. Language models (mostly) know what they know. CoRR , abs/2207.05221, 2022. doi: 10.48550/ARXIV .2207.05221. URL https://doi.org/10.48550/arXiv.2207.05221 . 12 Zhe Yang, Yichang Zhang, Yudong Wang, Ziyao... | https://arxiv.org/abs/2505.16170v2 |
P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging llm-as-a-judge with mt-bench and chatbot arena. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine, editors, Ad- vances in Neural Information Processing Systems 36: Annual Conference on Neural In- formation Processin... | https://arxiv.org/abs/2505.16170v2 |
Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023 , pages 12216–12235. Association for Computational Linguistics, 2023. doi: 10.18653/V1/2023.EMNLP-MAIN.751. URL https: //doi.org/10.18653/v1/2023.emnlp-main.751 . Yuqing Yang, Ethan Chern, Xipeng Qiu, Graham Neubig, and Pengfei... | https://arxiv.org/abs/2505.16170v2 |
. Qwen Team. Qwen3: Think deeper, act faster, 2025b. URL https://qwenlm.github.io/blog/ qwen3/ . Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, and Yongqiang Ma. Lla- mafactory: Unified efficient fine-tuning of 100+ language models. CoRR , abs/2403.13372, 2024. doi: 10.48550/ARXIV .2403.13372. URL ht... | https://arxiv.org/abs/2505.16170v2 |
reverse (e.g., “Who is Mary Lee Pfeiffer’s son?”, where the correct answer is Tom Cruise). We focus on the reverse questions. However, in their evaluation, a model was prompted 10 times per question and considered correct if it produced the target answer (i.e., the celebrity child) at least once. This evaluation cannot... | https://arxiv.org/abs/2505.16170v2 |
Output “True” if the assistant indicated that the initial answer does not fully satisfy the user’s question. Otherwise, output “False”. Here are a few examples for reference: Example 1: User Question: Name an actor who was born in Sheffield, United Kingdom. Assistant Response: Michael Palin. Initial Answer: Michael Pal... | https://arxiv.org/abs/2505.16170v2 |
those whose attention weights to the answer change most significantly between negative and positive belief steering. Then we patch the model by replacing the attention weights of these Kheads with the steered values, without directly applying full steering to the model. Patching Attention Value Vectors. We patch the at... | https://arxiv.org/abs/2505.16170v2 |
generalization to out-of-distribution data , as evidenced by their unsatisfactory performance on the CELEBRITY dataset. Additionally, for the WIKIDATA retraction direction, the mean hidden state representations may be unrepresentative due to (1) a limited number of retracted examples serving as positive examples, 20 an... | https://arxiv.org/abs/2505.16170v2 |
As shown in Table 18, the same belief steering directions remain effective after fine-tuning. Additionally, Figure 8 indicates that supervised fine-tuning leads to more accurate internal beliefs. Qwen2.5-7B Olmo2-7B Precision Recall Precision Recall Baseline 0.8824 0.1119 0.9881 0.1317 SFT 0.8350 0.7929 0.8869 0.8460 B... | https://arxiv.org/abs/2505.16170v2 |
Automated Feedback Loops to Protect Text Simplification with Generative AI from Information Loss Abhay Kumara Sri Krishna Nandiraju1, Gondy Leroy1, David Kauchak2, and Arif Ahmed1 1University of Arizona , Tucson , USA 2Pomona College , Claremont , USA {abhayna ndiraju, gondyleroy, arifahmed} @arizona.edu david.kauchak@... | https://arxiv.org/abs/2505.16172v1 |
text and leverages an automated feedback loop to ensure no critical information is omitted. 2 Related Work 2.1 Information Distribution in Healthcare Artificial Intelligence (AI) has evolved dramatically over recent decades, with generative AI emerging as one of the most tra nsformative technological developments of th... | https://arxiv.org/abs/2505.16172v1 |
were able to adapt to new settings via few -shot prompting techniques, but few -shot learning strategies still struggled on certain domain -specific tasks such as text simplification. With proper supervision [24], the usage of generative AI methods in healthcare can save time in preparing crucial biomedical reports and... | https://arxiv.org/abs/2505.16172v1 |
reflects more complete information. 3.1 Missing Information Identification We identify the missing information between the original text and the simpli fied text using tw o approaches. The first approach identifies words occurring frequently in the original text but are less frequent in the simplified text. Text is tok... | https://arxiv.org/abs/2505.16172v1 |
the entities i s shown in Fig. 2 : Fig. 2. The figure shows a prompt guiding the evaluation and selection of the most semantically valuable entities to enhance the simplified text . Approaches 4 and 5 are two control conditions in which we insert random information. These are nec essary to verify that adding any inform... | https://arxiv.org/abs/2505.16172v1 |
the cosine of the angle between the vector representations of the two texts(vector embeddings) in a high -dimensional space. Initially, each text is represented as a vector embedding in a 384 -dimensional de nse vector space using the embedding model all -MiniLM -L6-v2 from the sentence -transformers library. After rep... | https://arxiv.org/abs/2505.16172v1 |
allows us to draw conclusions more effectively rather than looking at the results from original representations themselves. 5 Results Using the original text and the simplified text, we calculated the average cosine similarity and ROUGE -1 scores for both the full tex t and summaries. The results are shown in Table 1. ... | https://arxiv.org/abs/2505.16172v1 |
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