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structured reasoning first 2. Follow up with a final answer, must in the <answer> </answer> tag. For example, <answer> xxx </answer>. 3. The answer must be succinct and final. For math problems, return the answer using LaTeX in the \boxed format. 4. If the question requires multiple steps or facts, break down your reas... | https://arxiv.org/abs/2505.17612v1 |
To compute arcsin −1 2 , we need to find the angle θsuch that sin(θ) =−1 2andθlies within the range of the arcsine function, which is [−π 2,π 2]. Code: 1import math 2 3# Calculate the arcsine of -1/2 4theta = math . asin ( -1/2) 5print ( theta ) Observation: -0.5235987755982989 Thought: The output from the code snipp... | https://arxiv.org/abs/2505.17612v1 |
of the function. Let’s evaluate f(θ)atθ=π 4. Code: 1# Evaluate the function at the critical point 2theta_critical = pi / 4 3f_theta_critical = f_theta . subs (theta , theta_critical ) 4print ( f_theta_critical ) Observation: π2/8 Thought: The minimum value of the function f(θ)atθ=π 4isπ2 8. Since the function is a quad... | https://arxiv.org/abs/2505.17612v1 |
8 9# Find the largest absolute value of the eigenvalues 10C = max( abs( eigenvalues )) 11 12print (C) Observation: 2.0 Thought: The code has executed and the value of C has been found. Now I will return the final answer using the final_answer tool. Code: 1final_answer ("\ boxed {2} ") Observation: 2(wrong) 26 Example D... | https://arxiv.org/abs/2505.17612v1 |
need to find out who founded the city of Euclid, Ohio. Code: 1founder_of_euclid = web_search ( query =" who founded the city of Euclid , Ohio ") 2print (f"The city of Euclid , Ohio was founded by { founder_of_euclid }.") Observation: The city of Euclid, Ohio was founded by (...) "South Euclid, Ohio" treaty with the Iro... | https://arxiv.org/abs/2505.17612v1 |
Alexandria : { founder_of_alexandria }") Observation: Who founded Alexandria: (...) Alexandria Alexandria is believed to have been founded by Alexander the Great in April 331 BC as (""Alexandreia""). Alexander’s chief architect for the project was Dinocrates. Alexandria was intended to supersede Naucratis as a Hellenis... | https://arxiv.org/abs/2505.17612v1 |
arXiv:2505.17613v1 [cs.AI] 23 May 2025MMMG: A Comprehensive and Reliable Evaluation Suite for M ultitask M ultim odal G eneration Jihan Yao∗1, Yushi Hu∗1, Yujie Yi1, Bin Han1, Shangbin Feng1, Guang Yang1, Bingbing Wen1 Ranjay Krishna1,2,Lucy Lu Wang1,2,Yulia Tsvetkov1,Noah A. Smith1,2,Banghua Zhu1 1University of Washin... | https://arxiv.org/abs/2505.17613v1 |
if ||avg_border_color - pink||2 > threshold: return 0 #check if border is solid ssim_score = SSIM(border, uniform(avg_border_color)) # if border is too wide penalty = ratio(inner_region == avg_border_color ) return ssim_score - penaltyTask : Create an image of a bike that has a square - shaped front wheel and a round -... | https://arxiv.org/abs/2505.17613v1 |
keyword by comparing the first word with the keyword), and (2) tasks with significant generation-evaluation gaps , where the generation step is challenging due to complex constraints, yet the evaluation step remains simple (e.g., generating an image of a snowman without a carrot nose can be challenging due to spurious ... | https://arxiv.org/abs/2505.17613v1 |
benchmark 24 open and proprietary multimodal generation models using the optimal evaluation methods identified in human studies. Partial results are shown in Figure 2; the rest are in Appendix D.2. We find that modality-unified autoregressive models (ARMs) surpass diffusion models in image generation, with GPT I MAGE [... | https://arxiv.org/abs/2505.17613v1 |
in accuracy when facing complex evaluation scenarios that require nuanced reasoning [Chen et al., 2025a]. To address these limitations, previous research incorporates extensive human preference data to enhance MLM accuracy [Xiong et al., 2024, Yao et al., 2025]. Our work is an orthogonal approach that carefully designs... | https://arxiv.org/abs/2505.17613v1 |
20 VLM Self Size Compare object sizes in the self-generated image. T T ,⊷ 20 VLM Self Relative Spatial Decide relative spacial relation in the generated image. T T ,⊷ 20 VLM Self Absolute Spatial Decide absolute spacial relation in the generated image. T T ,⊷ 20 VLM Self OCR Recognize the text in the generated image. T... | https://arxiv.org/abs/2505.17613v1 |
we remove instructions that are ambiguous (instructions with unclear or multiple interpretations), unrealistic (instructions that describe improbable or nonsensical scenarios), or redundant (instructions that closely resemble previously accepted examples). For instance, unrealistic instruction “ Generate an image of a ... | https://arxiv.org/abs/2505.17613v1 |
errors in scenarios with numerous objects. •Adding negative prompts helps alleviate visual hallucination. For instance, VLMs can easily overlook a constraint such as “ one basketball with a cube shape ,” whereas “ one basketball with a cube shape instead of a sphere ” forces the VLM to reject a spherical basketball. Im... | https://arxiv.org/abs/2505.17613v1 |
CLAPScore audio ,CLAPScore text, and employing GEMINI 2.5for acoustic question answering (AQA). CLAPScore audio computes the CLAP cosine similarity with reference audio, while CLAPScore textcomputes the similarity with reference audio captions. Following the optimal configurations identified in empirical studies, we ca... | https://arxiv.org/abs/2505.17613v1 |
and 3rd. This indicates that ARMs with stronger linguistic capabilities can better follow instructions. However, models struggle notably when generating objects with uncommon attributes and producing pairs of objects with unusual relationships, showing average accuracies of only 0.389 and 0.422 respectively. This under... | https://arxiv.org/abs/2505.17613v1 |
instructions may not fully align with real-world queries, MMMG achieves higher alignment with human prefer- ences. Such results suggest that evaluator alignment (i.e., the reliability of the evaluation method) may outweigh instruction distribution alignment (i.e., the extent to which benchmark tasks reflect real-world ... | https://arxiv.org/abs/2505.17613v1 |
Mm-iq: Benchmarking human-like abstraction and reasoning in multimodal models. arXiv preprint arXiv:2502.00698 , 2025. Dongping Chen, Ruoxi Chen, Shilin Zhang, Yaochen Wang, Yinuo Liu, Huichi Zhou, Qihui Zhang, Yao Wan, Pan Zhou, and Lichao Sun. Mllm-as-a-judge: Assessing multimodal llm-as-a-judge with vision-language ... | https://arxiv.org/abs/2505.17613v1 |
Representations , 2024. Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. Advances in Neural Information Processing Systems , 36: 52132–52152, 2023. Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A re... | https://arxiv.org/abs/2505.17613v1 |
Graham Neubig, Pengchuan Zhang, and Deva Ramanan. Genai-bench: A holistic benchmark for compositional text-to-visual generation. In Synthetic Data for Computer Vision Workshop@ CVPR 2024 , 2024. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. M... | https://arxiv.org/abs/2505.17613v1 |
Zhang, Zetong Zhou, Shuang Gong, et al. Judge anything: Mllm as a judge across any modality. arXiv preprint arXiv:2503.17489 , 2025. Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. Robust speech recognition via large-scale weak supervision. In International conference on mach... | https://arxiv.org/abs/2505.17613v1 |
et al. Mmie: Massive multimodal interleaved comprehension benchmark for large vision-language models. In Adaptive Foundation Models: Evolving AI for Personalized and Efficient Learning , 2025. Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang... | https://arxiv.org/abs/2505.17613v1 |
user experiences. To mitigate this, we intend to dynamically expand and update our benchmark tasks in real-time as more powerful and reliable evaluation models become available. We also include tasks that we considered commonly used but abandoned due to infeasible evaluation in Appendix B.2. Dependence on Proprietary M... | https://arxiv.org/abs/2505.17613v1 |
use them as reference speeches for voice replication tasks. The remaining tasks are generated from GPT-4 Owith manually designed templates. B.2 Excluded Tasks We present the remaining 27 tasks we considered from our initial task set in Table 4. We ex- clude “Format Color”, “Format Symmetric”, “Speech Transcribing”, “Sp... | https://arxiv.org/abs/2505.17613v1 |
the animal that is mostly likely to make the sound in <audio_0>. T,/volume-down⊷ Table 4: Tasks that are not included in MMMG .Tdenotes text modality, ⊷for image modality, /images for multiple images, /volume-downfor audio and /volume-down/volume-downfor multiple audios. We hope to incorporate these tasks when reliable... | https://arxiv.org/abs/2505.17613v1 |
2025], through the “stabilityai/stable-audio-open-1.0” checkpoint on Huggingface, and AUDIO LDM 2 [Liu et al., 2024], through the “cvssp/audioldm2-large” checkpoint on Huggingface, capable of generating both sound and music. We also include sound generation models: AUDIO GEN[Kreuk et al., 2022], through the official im... | https://arxiv.org/abs/2505.17613v1 |
a more central way) Explain step by step and end your answer with Answer: [only an optional letter]. ” Multiple choice questions can boost VLM’s performance on spacial reasoning tasks. We employ this prompt for absolute spatial relationship and self absolute spatial relationship recognizing tasks. •Left-Right Spacial R... | https://arxiv.org/abs/2505.17613v1 |
3.The post-processing system replaces this placeholder with an image created or edited based on your instructions. 4.Naturally incorporate references to the generated or edited image in your ongoing conversation. When crafting image prompts, follow these guidelines: For image prompts: • Provide detailed, specific descr... | https://arxiv.org/abs/2505.17613v1 |
starting at 0). •Important: Only reference audio that itself does not reference previous audio to avoid circular references. Provide concise, direct responses precisely following user instructions. In multi-speaker scenar- ios, maintain consistent and distinctive voice characteristics for each speaker. Always maintain ... | https://arxiv.org/abs/2505.17613v1 |
structural consistency is assessed by computing the SSIM between the targeted region and an artificially generated solid region filled with the calculated average RGB color, confirming color uniformity. Finally, the procedure examines over-fill by evaluating the margin area surrounding the targeted region and computing... | https://arxiv.org/abs/2505.17613v1 |
Sound Inclusion. “Is the given audio about [sound]? Reminder: 1. Chose yes when [sound] is the main sound existing in the audio. 2. [sound] should be common real-world sound without distortion. ” We employ this question for all sound generation tasks. Speaker Similarity. “Are the speeches coming from the same speaker? ... | https://arxiv.org/abs/2505.17613v1 |
improved, we conducted experiments with GEMINI IMAGE using the planning system prompt detailed in Table 13. The experimental results, summarized in Table 14, indicate that incorporating system prompts emphasizing modality count and order does not consistently lead to positive outcomes. Generally, adding a system prompt... | https://arxiv.org/abs/2505.17613v1 |
for Pearson correlation. We report Word Accuracy for text rendering, text editing and OCR tasks. Best results are in bold .MMMG achieves an average best human agreement of 0.943 with average inter- annotator agreement being 0.971. GPT-4 Ois the most human-aligned image evaluation model while CLAPScore audio is the most... | https://arxiv.org/abs/2505.17613v1 |
0.213 0.438 0.100 0.850 Self Color 0.000 0.000 0.000 0.413 0.663 0.700 Self Size 0.000 0.000 0.263 0.338 0.338 0.600 Self OCR 0.000 0.000 0.101 0.626 0.312 0.958 Self Relative Spatial 0.000 0.000 0.250 0.475 0.538 0.725 Self Absolute Spatial 0.000 0.000 0.100 0.600 0.475 0.775 Interleaved Math 0.000 0.000 0.000 0.013 0... | https://arxiv.org/abs/2505.17613v1 |
order and number. TaskGEMINI IMAGE w/ promptGEMINI IMAGE w/o prompt Semantic Consistency 0.263 0.013 Multi-Angel Consistency 0.135 0.352 Multi-View Consistency 0.094 0.143 Compose Consistency 0.013 0.000 Decompose Consistency 0.000 0.013 Interleaved Object Adding 0.399 0.545 Interleaved Color Modifying 0.486 0.609 Text... | https://arxiv.org/abs/2505.17613v1 |
longer than only one snake. Good Example: IDEOGRAM 2 Evaluation: ✔Bad Example: GEMINI IMAGE Evaluation: ✘ Analysis : The snake is longer than the nail. Figure 10: Examples for the task: Comparison Relation 30 Universal Relation Instruction : Generate an image of a toy box where all toys are animals except one car. Good... | https://arxiv.org/abs/2505.17613v1 |
left side of a white desk and exactly one coffee mug on the right. The sticky note should have “Stay bold, chase dreams” written on it while the coffee mug should have “Coffee fuels infinite morning possibilities” printed on it. The text should be written in a clear, standard, easily readable typeface without any artis... | https://arxiv.org/abs/2505.17613v1 |
image of the floral patterned seat cushion rotated 60 degrees to the right, revealing much of its right side and significantly obscuring the left. Generating an image of the floral patterned seat cushion rotated 30 degrees to the left, showing more of its left side and slightly obscuring the right. Generating an image ... | https://arxiv.org/abs/2505.17613v1 |
clear glass bowl on the light wooden surface. Evaluation: ✘ Analysis : Fail to follow the required order of adding objects and generate incorrect num- ber of images. Figure 22: Examples for the task: Composition Consistency 41 Decomposition Consistency Instruction : Create three images that sequentially show the result... | https://arxiv.org/abs/2505.17613v1 |
on the meter, with all other elements in the image remaining unchanged. Good Example: GEMINI IMAGE Evaluation: ✔, 0.911 (image similarity)Bad Example: GPT-4 O+ GPT I MAGE Evaluation: ✘ Analysis : No obviously visible blue sticker is added. Figure 27: Examples for the task: Image Editing - Object Adding 46 Image Editing... | https://arxiv.org/abs/2505.17613v1 |
Self OCR Instruction : Create an image of a whiteboard displaying a motivational quote. The quote must be between 5 and 10 words long, written in a clear, standard, and easily readable typeface without any artistic alterations. The quote should encourage people to follow their dreams. Once the image is generated, retur... | https://arxiv.org/abs/2505.17613v1 |
question mark. Unfortunately all the models we evaluate perform poorly on this task. Here we will demonstrate our gold reference images. Reference Answer: Evaluation: ✔Bad Example: GEMINI 2.5 +GPT I MAGE Response: To determine the pattern that replaces the question mark, let’s analyze the positions of the white circle ... | https://arxiv.org/abs/2505.17613v1 |
Two ellipses with a fill color of #D1D3D4 are used to create the ears. These are positioned at the top left and right of the face. . . . 8.Mouth : A path with a fill color of #915A34 is used to create the mouth, adding detail to the expression. 9.Background Elements : The SVG in- cludes additional paths and ellipses to... | https://arxiv.org/abs/2505.17613v1 |
a mouth click only in the 2nd second, not the end. Evaluation: ✘ Figure 39: Examples for the task: Sound Begin-End Sound Positional Inclusion Instruction : Create an audio of a city street, ensuring a police car siren is included in the first half. Good Example: AUDIO GEN Audio Description: an audio of a city street wi... | https://arxiv.org/abs/2505.17613v1 |
oice Attribution Instruction : Generate an audio of a man speaking rapidly in a low-pitched voice, saying, “The detective carefully examined the crime scene, noting every detail that could lead him to the truth, knowing that even the smallest clue might be the key to solving the mystery.” Good Example: GEMINI 2.5+VOXIN... | https://arxiv.org/abs/2505.17613v1 |
speech transcript: “We must act now. Every action counts. We can make a difference together if we remain united. Progress comes from shared responsibility.”) Good Example: GEMINI 2.5+VOICE LDM Speech Transcript: We must act now. Every action counts. We can make a difference together if we remain united. Join hands, bre... | https://arxiv.org/abs/2505.17613v1 |
ob- jections. Adapt your language, examples, and level of detail to resonate specifically with them. Frame your core message in a way that directly addresses their interests and demonstrates clear value from their perspec- tive, ensuring your arguments are supported by credible evidence and logical reasoning. (speech) ... | https://arxiv.org/abs/2505.17613v1 |
arXiv:2505.17615v1 [cs.LG] 23 May 2025Large language model as user daily behavior data generator: balancing population diversity and individual personality Haoxin Li*, Jingtao Ding∗, Jiahui Gong, Yong Li Department of Electronic Engineering, Tsinghua University dingjt15@tsinghua.org.cn; liyong07@tsinghua.edu.cn Abstrac... | https://arxiv.org/abs/2505.17615v1 |
commuting and entertainment, indicating that these models have already captured significant behavioral knowledge through pretraining. In this work, we explore the synthetic data gener- ation capabilities of large language models (LLMs) for user behavior modeling. To meet practical re- quirements, modern user behavior p... | https://arxiv.org/abs/2505.17615v1 |
al. (2023) AnnoLLM, an LLM-powered annotation system. It first prompts LLMs to explain the reasoning behind a ground truth label, then uses these explanations to create a few-shot chain-of-thought prompt for annotating unlabeled data. However, existing work has not adequately ad- dressed the balance between population ... | https://arxiv.org/abs/2505.17615v1 |
experiment. User behavior prediction aims to fore- cast future user behavior based on its past Ievent series, which can be formed as, ˆbt=f(xt−I, xt−I+1, ..., x t−1) (2)4 BehaviorGen Framework 4.1 Data Generation Procedure 4.1.1 Data generation Process Role Setting : In this stage, the Large Language Model (LLM) is ass... | https://arxiv.org/abs/2505.17615v1 |
method enhances the model’s predictive ac- curacy concerning user behavior, enabling a more robust capture of behavioral patterns, as confirmed in subsequent experiments. The augmented dataset allows the model to better generalize and predict user behavior across diverse scenarios, ultimately improving its effectivenes... | https://arxiv.org/abs/2505.17615v1 |
users. 5.1.2 Metrics To assess model performance, we employ four widely used metrics: precision ( Pre), recall ( Rec), and NDCG(N) (Ding et al., 2018, 2020). NDCG gauge classification accuracy and ranking quality, respectively, while Pre and Rec evaluate the aver- age prediction accuracy for each intent, indicating the... | https://arxiv.org/abs/2505.17615v1 |
0.366 0.624 0.620 0.167 0.283 0.524 0.557 0.136 0.174 0.433 0.467 UPC_SDG 0.424 0.457 0.676 0.670 0.384 0.417 0.630 0.632 0.188 0.295 0.528 0.558 0.130 0.169 0.438 0.472 Ours 0.447 0.480 0.702 0.694 0.426 0.450 0.655 0.659 0.213 0.315 0.543 0.570 0.201 0.186 0.454 0.479 Improvement 2.5% 1.9% 2.6% 1.3% 1.9% 0.2% -1.8% -... | https://arxiv.org/abs/2505.17615v1 |
to real user data. This balance between diversity and fidelity enabled the model to gener- alize more effectively. As presented in Table 1, models pre-trained with a mix of real and synthetic data exhibited significant improvements in accu- racy and recall, indicating that synthetic data intro- duces sufficient variabi... | https://arxiv.org/abs/2505.17615v1 |
BD, JSD, and Pass@1. The highest value for KSPandBLEU and the lowest value for BD andJSD are highlighted. We use following metrics: KS_P measures the discrepancy between the distributions of generated and real data, with higher values indicating bet- ter alignment. BLEU assesses the n-gram over- lap between generated a... | https://arxiv.org/abs/2505.17615v1 |
Figure 2(a) and Figure 2(b) present the real intent distributions of these users alongside the intent dis- tributions generated from synthetic data. And the population level intent distribution is shown in grey. For both User A and User B, the real intent distri- bution (shown in blue) demonstrates a pronounced deviati... | https://arxiv.org/abs/2505.17615v1 |
daily activities will be key to gen- erating higher-quality synthetic data (Ding et al., 2024; Zhang et al., 2025). References Martin Abadi, Andy Chu, Ian Goodfellow, H. Bren- dan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep learning with differential pri- vacy. In Proceedings of the 2016 ACM SIGSAC Co... | https://arxiv.org/abs/2505.17615v1 |
arXiv preprint arXiv:2407.18932 . Fan Liu, Zhiyong Cheng, Huilin Chen, Yinwei Wei, Liqiang Nie, and Mohan Kankanhalli. 2022. Privacy- preserving synthetic data generation for recommen- dation systems. In Proceedings of the 45th Inter- national ACM SIGIR Conference on Research and Development in Information Retrieval , ... | https://arxiv.org/abs/2505.17615v1 |
Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, and Chao Zhang. 2023. Large language model as attributed training data generator: A tale of diversity and bias. Advances in neural information processing systems . Yuan Yuan, Jingtao Ding, Depeng Jin, and Yong Li. 2025. Learning the complexity of urb... | https://arxiv.org/abs/2505.17615v1 |
3 " role ": " system ", 4 " content ": """ 5You are an assistant generating behavioral data based on given user behavior and profile data . I will provide you with a subset of real behavioral data in the format [ weekday , timestamp , loc , intent ]. 6 7Your task : 81. Generate behavioral data for one month ( minimum 9... | https://arxiv.org/abs/2505.17615v1 |
defined as the one with the highest overlapping ratio. We calculate the overlap- ping ratio distribution of all the generated tra- jectories with the most similar real trajectories mentioned before. The results can also be ex- tended by considering more similar trajectories, (a) Smartphone (b) Tencent Figure 5: Privacy... | https://arxiv.org/abs/2505.17615v1 |
distribution of zwith the data Das the input. Smaller values of ϵandδprovide stronger pri- vacy guarantees. In our experiment, we exam- ine the privacy budget of our proposed model from the perspective of changes in the overlap- (a) Smartphone (b) Tencent Figure 6: Privacy evaluation in terms of Membership inference at... | https://arxiv.org/abs/2505.17615v1 |
arXiv:2505.17616v1 [cs.CL] 23 May 2025Runaway is Ashamed, But Helpful: On the Early-Exit Behavior of Large Language Model-based Agents in Embodied Environments Qingyu Lu♢, Liang Ding♡, Siyi Cao♢, Xuebo Liu❄, Kanjian Zhang♢♠* Jinxia Zhang♢, Dacheng Tao✿ ♢Southeast University♡The University of Sydney♠Southeast University... | https://arxiv.org/abs/2505.17616v1 |
becomes even more critical in real-world settings, where repeated mistakes by embodied agents can waste energy, cause wear-and-tear, or even damage physical objects in the environment. Therefore, incorporating built-in self-awareness mechanisms can help agents detect when progress has stalled, enabling early self-refle... | https://arxiv.org/abs/2505.17616v1 |
generalize across vari- ous LLM-based agents and task settings. We further demonstrate the practical value of our approach by introducing post-trial strategies that leverage stronger agents to enhance over- all performance. This study is an initial step toward exploring early-exit behavior in LLM-based agents. Our ap- ... | https://arxiv.org/abs/2505.17616v1 |
agents in embodied environments is evaluated using Success Rate and 1We set k= 1 to enable timely detection in our experi- ments. In practice, larger values (e.g., k= 2–5) can be used to reduce computational overhead.Progress Rate. To intuitively demonstrate the be- havior of the early-exit mechanism on LLM-based agent... | https://arxiv.org/abs/2505.17616v1 |
25% loss in progress. Progress Degradation ranges from 0 (no degradation) to PRref(complete loss of progress). A higher PD indicates greater performance loss. In the trivial case, PD= 0im- plies no degradation, while PD=PRrefindicates complete progress failure (e.g., all environments terminate at the first step). 3 Exp... | https://arxiv.org/abs/2505.17616v1 |
57.3 6.5 6.6 25.1 7.8 34.5 13.0 9.4 32.3 Llama3.1-70B-Instruct - - 76.1 81.1 2.3 - 19.0 49.1 62.8 8.8 - 26.4 34.4 67.5 15.7 - 31.4 ✓ ✗ 61.2 67.4 1.4 17.4 13.8 36.6 53.1 7.4 18.0 18.2 18.9 59.8 8.3 12.3 21.2 ✗ ✓ 70.2 79.3 1.4 8.7 13.4 42.0 59.3 4.5 12.9 13.3 27.8 63.6 6.0 9.0 17.4 ✓ ✓ 80.6 84.0 1.5 5.8 17.0 40.2 57.8 6.... | https://arxiv.org/abs/2505.17616v1 |
whereas it significantly degrades the performance of Mistral-24B-Instruct . Conversely, Mistral-24B-Instruct benefits more from the extrin- sic method ("Ext."). This is possibly because the larger Mistral LLMs is more sensitive to intrinsic cues, resulting in premature termination, whereas extrinsic method provide more... | https://arxiv.org/abs/2505.17616v1 |
(PD)Maximum Step ReachedPerfect Early-ExitToo Early RSPD Too Late RSPD Figure 4: Redundant Steps and Progress Degrada- tion measured in a failure case with 3 out of 4 subgoals completed. The metrics vary as the early-exit mecha- nism is triggered at different steps. Perfect Early-Exit Scenario The ideal early exit scen... | https://arxiv.org/abs/2505.17616v1 |
as the weak agent which achieves a 58.2% success rate under a ReAct-style format and 57.5% when paired with an external early-exit mechanism (seen in Table 1), andLlama3.1-70B-Instruct as the strong agent. Setup In baseline, the weaker agent executes up to 40 steps regardless of progress. With the ex- trinsic early-exi... | https://arxiv.org/abs/2505.17616v1 |
for better token efficiency in agent-collaboration sce- narios. In this work, we apply early exit to LLM- based agents in embodied environments, proposing an efficient and robust method adaptable to various agents, along with metrics to assess performance. Agent Verification and Evaluation Traditional benchmarks like A... | https://arxiv.org/abs/2505.17616v1 |
this pa- per are reported accurately and objectively. No human participants were involved as evaluators or case studies in this work.References Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4... | https://arxiv.org/abs/2505.17616v1 |
Yuan, Yusheng Zhao, Jun- wei Yang, Yiyang Gu, Bohan Wu, Binqi Chen, Ziyue Qiao, Qingqing Long, et al. 2025. Large language model agent: A survey on methodology, applications and challenges. arXiv preprint . Siqi Ouyang and Lei Li. 2023. AutoPlan: Automatic planning of interactive decision-making tasks with large langua... | https://arxiv.org/abs/2505.17616v1 |
Bert loses patience: Fast and robust inference with early exit. NeurIPS . A Recommended Early-Exit Approaches Based on our experimental results and analysis, we provide a set of recommendations for selecting suitable early-exit approaches for specific LLMs. These guidelines are summarized in Table 3 and can serve as a ... | https://arxiv.org/abs/2505.17616v1 |
tion} Do not include any additional text or expla- nations in your response. Early-Exit Approach Strict Condition Modest Condition Intrinsic Early-Exit Once the environment appears complete or no further progress is likely, include ’EXIT’ in your action to end the task without delay.If you believe the environment is co... | https://arxiv.org/abs/2505.17616v1 |
arXiv:2505.17625v1 [cs.CL] 23 May 2025Enhancing Large Vision-Language Models with Layout Modality for Table Question Answering on Japanese Annual Securities Reports 1stHayato Aida Stockmark Tokyo, Japan hayato.aida@stockmark.co.jp2ndKosuke Takahashi Stockmark Tokyo, Japan kosuke.takahashi@stockmark.co.jp3rdTakahiro Omi... | https://arxiv.org/abs/2505.17625v1 |
Ablation studies further highlight the critical role of these auxiliary modalities in correctly identifying relevant cells, providing new insights into effective multimodal strategies for table understanding in real-world business documents. II. R ELATED WORK Visual document understanding encompasses the extraction and... | https://arxiv.org/abs/2505.17625v1 |
of LLMs utilizing Transformer de- coders with layout awareness, QA datasets incorporating lay- out information have also been proposed. LayoutLLM [8] introduces a QA dataset that leverages document images along with the text and layout information within them, while also presenting a baseline architecture. The construc... | https://arxiv.org/abs/2505.17625v1 |
out cases where table rendering failed, we obtained a final dataset consisting of 10,278 training examples and 1,303 test examples. This dataset serves as the foundation for evaluating our proposed TableCellQA task under various modality configurations. C. Model Construction LVLMs typically accept only images as input.... | https://arxiv.org/abs/2505.17625v1 |
3. Overall system architecture Fig. 4. The architecture of our LVLM. Each text token is paired with its corresponding layout feature (e.g., Layout 1and Text 1), where the layout is represented by bounding box coordinates. Layout features are encoded via an MLP and combined with text and image features as input to the L... | https://arxiv.org/abs/2505.17625v1 |
the advantage of relying solely on clean text embeddings aligned with layout information, without introducing redundant visual representations where the textual content is already explicitly provided. However, it should be noted that these differences are within a margin of less than 0.5%, and may fall within the natur... | https://arxiv.org/abs/2505.17625v1 |
in JSON is less common in real-world applications, and it is expected that the underlying models are pre-trained on a larger volume of HTML tables than JSON ones. These factors likely account for the performance differences observed among the structured table text formats. Although structured HTML tables demonstrated t... | https://arxiv.org/abs/2505.17625v1 |
Accuracy and ANLS scores decreased. This finding suggests that the inherent capability of a general-purpose LVLM to comprehend struc- tured visual information may already be sufficient—or even preferable—for TableCellQA tasks, compared to specialized pre-training that could unintentionally limit the model’s gen- eraliz... | https://arxiv.org/abs/2505.17625v1 |
V Jawahar. DocVQA: A dataset for VQA on document images. arXiv [cs.CV] , July 2020. [10] OpenAI. Gpt-4o system card. https://arxiv.org/abs/2410.21276, 2024. Accessed: 2025-04-28. [11] Seunghyun Park, Seung Shin, Bado Lee, Junyeop Lee, Jaeheung Surh, Minjoon Seo, and Hwalsuk Lee. Cord: A consolidated receipt dataset for... | https://arxiv.org/abs/2505.17625v1 |
GIM: Improved Interpretability for Large Language Models Joakim Edin1,3Róbert Csordás2Tuukka Ruotsalo3,4 Zhengxuan Wu2Maria Maistro3Jing Huang2Lars Maaløe1 1Corti2Stanford University3Copenhagen University4LUT University je@corti.ai Abstract Ensuring faithful interpretability in large language models is imperative for t... | https://arxiv.org/abs/2505.17630v1 |
is reduced, the softmax activation function increases the attention weight of the other positions, maintaining similar output signals (see Figure 1). This attention self-repair systematically causes both perturbation-based methods and gradient methods to underestimate the importance of all components contributing to th... | https://arxiv.org/abs/2505.17630v1 |
[ 16], Integrated Gradients [ 5], and DeepLIFT [ 17] attempt to address these limitations by modifying gradient flows, but empirically struggle with language models [18, 19, 10]. Several studies propose specialized backpropagation rules for the transformer architecture. Trans- formerLRP [ 9] treats the layer normalizat... | https://arxiv.org/abs/2505.17630v1 |
the model’s final output logit zwith respect to the attention score ajwill be near-zero. We can define the gradient as: ∂z ∂aj=sj ∂z ∂sj(1−sj)−X k̸=j∂z ∂sksk (3) where∂z ∂sjis the gradient of the output logit with respect to the attention weight sj. This is computed as follows: ∂z ∂sj=∂z ∂o·Vj (4) To demonstrate t... | https://arxiv.org/abs/2505.17630v1 |
increase of entropy). In other words, large attention weights decrease while small weights increase. This increases our set of positions with significant weights Iϵ={k:sk> ϵ}. Recall that for self-repair to occur, the gradients∂z ∂sjmust be approximately uniform across all positions in Iϵ. By expanding Iϵto include mor... | https://arxiv.org/abs/2505.17630v1 |
where βis a threshold parameter. For each identified attention vector, we measure the joint effect by ablating the two largest attention scores simultaneously versus the sum of their individual ablation effects. If the joint effect is larger than the sum of the individual effect, the attention scores exhibit OR-gate-li... | https://arxiv.org/abs/2505.17630v1 |
(y-axis) versus the sum of their individual ablation effects (x-axis). The consistent gap above the diagonal, especially prominent for orange-highlighted instances (top 5% strongest interactions), provides clear empirical evidence of self-repair: individual attention scores show disproportionately small effects when ab... | https://arxiv.org/abs/2505.17630v1 |
0.49 0.64 0.44 0.00 0.46 0.35 0.32 0.58 0.56 0.61 0.64 0.72 DeepLIFT 0.36 0.29 0.55 0.36 0.41 0.29 0.42 0.52 0.45 0.59 0.70 0.73 0.74 0.73 TransformerLRP 0.54 0.14 0.51 0.58 0.33 0.20 0.30 0.38 0.24 0.59 0.57 0.67 0.66 0.71 AttnLRP - - 0.68 0.70 0.60 0.85 0.59 - - 0.30 0.47 0.42 0.33 0.71 GIM 0.64 0.48 0.68 0.64 0.65 0... | https://arxiv.org/abs/2505.17630v1 |
beneficial for Qwen-2.5 1.5B, where it occasionally reduces attribution faithfulness. Qwen-2.5 1.5B had a hundred-fold larger gradients than the other models, which perhaps is problematic for TSG (see Figure 11). Comprehensive feature attribution results for all modification combinations are available in Table 2. 7 Dis... | https://arxiv.org/abs/2505.17630v1 |
evaluations across multiple models and datasets show that GIM consistently outperforms existing circuit identification and feature attribution methods in faithfulness. By accounting for self-repair during backpropagation, GIM better quantifies the importance of model components, advancing our understanding of neural ne... | https://arxiv.org/abs/2505.17630v1 |
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and Editing Factual Associations in GPT. Advances in Neural Information Processing Systems , 35:17359–17372, December 2022. [14] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep Inside Convolutional Networks: Visualis- ing Image Classificati... | https://arxiv.org/abs/2505.17630v1 |
When Finding Model Mechanisms, July 2024. [29] Aaquib Syed, Can Rager, and Arthur Conmy. Attribution Patching Outperforms Automated Circuit Discovery, November 2023. [30] Judea Pearl. Causality . Cambridge University Press, September 2009. ISBN 978-0-521-89560-6. [31] Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom ... | https://arxiv.org/abs/2505.17630v1 |
0.45 0.47 0.37 0.18 0.53 0.24 0.30 0.62 0.72 + Grad norm + LN freeze 0.53 0.68 0.72 0.69 0.66 0.07 0.28 0.12 0.12 0.29 + Grad norm + LN freeze + TSG 0.59 0.69 0.72 0.68 0.61 0.03 0.22 0.10 0.09 0.23 FEVERGradient X Input 0.03 0.39 0.53 0.43 0.47 0.47 0.56 0.63 0.59 0.73 + LN freeze 0.18 0.49 0.52 0.37 0.58 0.38 0.48 0.... | https://arxiv.org/abs/2505.17630v1 |
0.64 0.75 0.73 0.64 + LN freeze 0.45 0.50 0.46 0.39 0.75 0.48 0.56 0.75 0.72 0.69 + Grad norm 0.48 0.38 0.55 0.39 0.69 0.47 0.62 0.75 0.69 0.64 + TSG 0.35 0.50 0.47 0.59 0.71 0.59 0.61 0.76 0.78 0.66 + Grad norm + TSG 0.47 0.48 0.55 0.63 0.80 0.48 0.51 0.68 0.75 0.61 + LN freeze + TSG 0.37 0.47 0.52 0.42 0.51 0.56 0.63... | https://arxiv.org/abs/2505.17630v1 |
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