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Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Qu, Hui L... | https://arxiv.org/abs/2505.17123v2 |
and Igor Mordatch. Language models as zero- shot planners: Extracting actionable knowledge for embodied agents. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, and Sivan Sabato, editors, International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA , vo... | https://arxiv.org/abs/2505.17123v2 |
contamination free evaluation of large language models for code. In The Thirteenth International Conference on Learning Representations , 2025. [18] Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi. Maieutic prompting: Logically consistent reasoning with recursiv... | https://arxiv.org/abs/2505.17123v2 |
Representations , 2023. [30] Norbert M Seel. Encyclopedia of the Sciences of Learning . Springer Science & Business Media, 2011. [31] Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. Commonsenseqa: A question answering challenge targeting commonsense knowledge. In Jill Burstein, Christy Doran, and Th... | https://arxiv.org/abs/2505.17123v2 |
Twelfth International Conference on Learning Representations , 2024. [40] Yuzhuang Xu, Shuo Wang, Peng Li, Fuwen Luo, Xiaolong Wang, Weidong Liu, and Yang Liu. Exploring large language models for communication games: An empirical study on werewolf. CoRR , abs/2309.04658, 2023. [41] An Yang, Baosong Yang, Beichen Zhang,... | https://arxiv.org/abs/2505.17123v2 |
interactive game environments we designed naturally lend themselves to reinforcement learning applications. While our current work focuses on evaluation, these environments could serve as valuable training grounds for developing specialized reasoning capabilities through reward-based learning. Future work will explore ... | https://arxiv.org/abs/2505.17123v2 |
given exclusion rules. Case D.3: GuessMax Problem Template Let’s play Guess The Maximums! Rules: 1. Hidden array A[1...50]contains numbers from 1 to 50 2. You need to guess nnumbers forming the password 3. For password position i, you are given Si= subset of positions to exclude 4. Password [i]= max value among all pos... | https://arxiv.org/abs/2505.17123v2 |
the formats shown above 3. Explain your reasoning before each query 4. All distances are precise to 10−10 Remember: - Circle parameters are integers - Rays start from origin (0,0) - Think carefully about ray directions - Use geometric properties to deduce circle location - Distance is 0 when ray intersects circle Ready... | https://arxiv.org/abs/2505.17123v2 |
a query: Format: “My Query: S|T|v” where: -Sis your first set of vertices (space-separated numbers) -Tis your second set of vertices (space-separated numbers) -vis the vertex you want to check Example: “My Query: 1 2 | 3 | 2” Response: You will receive the number of vertex pairs (s, t)where: -sis from set S -tis from s... | https://arxiv.org/abs/2505.17123v2 |
Query Game! Your task is to find specific positions in a hidden permutation through median queries. Rules: 1. There is a hidden permutation pof length n(numbers 1 to n) 2. You can make queries about subsequences of even length 20 3. Each query returns the two middle values (medians) of your chosen subsequence 4. Your g... | https://arxiv.org/abs/2505.17123v2 |
D.16: MinMax Difficulty Levels Easy: n= 5, Medium: n= 6, Hard: n= 7 WordGuessing In this task, models need to discover a hidden n-letter word through strategic guesses. Each guess receives feedback indicating correct letters, misplaced letters, and wrong letters, helping narrow down the target word. Case D.17: WordGues... | https://arxiv.org/abs/2505.17123v2 |
You: “My Query: XOR 2 4” Me: “3” (result of XOR operation) You: “My Answer: 0 0 2 3” Instructions: 1. Make queries based on previous results 2. Use exactly the formats shown above 3. Explain your reasoning before each query Remember: - All array elements are between 0and{n−1} - Position indices start from 1 - Think car... | https://arxiv.org/abs/2505.17123v2 |
metal arcs after your rotations. Rules: 1. Lock Structure: -{n}concentric rings numbered 0 to {n−1} - Each ring has {n∗m}sections (0 to {n∗m−1}) 24 - Each section can be empty or contain metal - Rings can rotate independently 2. Metal Arcs: - Each ring has one metal arc - Each arc covers exactly 6 consecutive sections ... | https://arxiv.org/abs/2505.17123v2 |
row Example Interaction: You: “My Query: 1 4” Me: “3” (3 students raised hands) You: “My Query: 3 5” Me: “2” (2 students raised hands) You: “My Answer: 2” Me: “Correct” Instructions: 1. Make queries based on previous responses 2. Use exactly the formats shown above 3. Explain your reasoning before each query Remember: ... | https://arxiv.org/abs/2505.17123v2 |
a set of numbers: - You choose any non-empty set of numbers - System will tell you if xis in that set (“YES”) or not (“NO”) - WARNING: Responses might be lies! - BUT: At least one answer out of any two consecutive queries is truthful 3. For guesses: - You can directly guess what xis - Guesses are always answered truthf... | https://arxiv.org/abs/2505.17123v2 |
(This answer includes ALL tiles, including the ones you added!) Instructions: 1. Make queries to add tiles strategically 2. Use exactly the formats shown above 3. Explain your reasoning before each addition 4. Watch how combinations change Remember: - Each value appears 0 to {n}times - Same-value tiles count as differe... | https://arxiv.org/abs/2505.17123v2 |
current q 2. To submit final answer: Format: “My Answer: p1p2...p{n}” where p1top{n}form your guessed permutation Example: “My Answer: 4 2 1 3” Example Interaction: Initial q= [1,2, ...,{n}] You: “My Query: 3” Me: “3” [qupdates based on p] You: “My Query: 2” Me: “2” [qupdates again] You: “My Answer: 4 2 1 3” Instructio... | https://arxiv.org/abs/2505.17123v2 |
Zero Finding Game! Your task is to find the {k}-th zero in a hidden binary array through range sum queries. Rules: 1. There is a hidden array of {n}elements (all 0s and 1s) 2. You need to find the {k}-th zero 3. Each time you find a non-target zero (not {k}-th), it turns into 1 4. The game continues until you find the ... | https://arxiv.org/abs/2505.17123v2 |
3. Movement Rules: - Each move returns your new position (x, y) - If move is invalid (out of grid), position stays same - Grid boundaries: 1≤row≤ {n},1≤column ≤ {m} - If you hit dangerous cell, returns (−1,−1)and game ends - When you reach finish cell ({finish _pos}), game ends successfully Move Types: 1. To make a mov... | https://arxiv.org/abs/2505.17123v2 |
One leads back (has your flag) - One is unexplored (no flag) You: “My Choice: 1” - Moving to unflagged junction Instructions: 1. Give your reasoning before each choice 2. Wait for response before next move 3. Use exactly the format shown above Remember: - Real junction numbers are hidden - Connected junctions appear in... | https://arxiv.org/abs/2505.17123v2 |
1. Control a candy through a n∗nfactory grid 2. Start at (1,1)with white color (W), reach (n, n) 3. Hidden devices in cells marked by X: - Dye Machines: R(red), G(green), B(blue) 36 - Empty cells (-) 4. Bleach Machine is shown as W(white) in the map and it can reset any color to white 5. Each level gives a target color... | https://arxiv.org/abs/2505.17123v2 |
Move one step in its direction unless that step would hit a boundary - Danger zone: You lose - Neutral space: Stay there 3. If magnetic field pushed you to another magnetic field, repeat step 2 Current Grid Layout (with coordinates): {grid_str} {position _str} Query Types: 1. To make a move: Format: “My Move: X” where ... | https://arxiv.org/abs/2505.17123v2 |
sure you’re on the highest value treasure - You only get one collection attempt Response Types: - If you find a treasure: “TREASURE v” (vis the treasure’s value) - If empty cell: “EMPTY dir” (dirindicates which region contains nearest treasure) - If invalid move: “INV ALID_MOVE” Example interaction: You: “My Choice: 2 ... | https://arxiv.org/abs/2505.17123v2 |
D.55: ColorMagic Problem Template Let’s play Color Magic! Your task is to make all cells the same color through magical color transformations. Rules: 1. You have a n∗ngrid where each cell contains one of three colors: Red(R), Blue(B), Yellow(Y) 2. There are three magic operations with unknown number assignments (1, 2, ... | https://arxiv.org/abs/2505.17123v2 |
Exchanges elements within a compound - High energy might cause multiple swaps - Example: ABC →CBA (normal) or BAC (partial) - Format: “My Move: X N ” (Xis a compound, and N= 1/2/3/4) EXTRACT: - Takes out one element from a compound - Usually the last element, but might extract a random element - Example: ABC →C (normal... | https://arxiv.org/abs/2505.17123v2 |
the format shown above 3. Explain your reasoning before each guess Remember: - Each vertex is numbered from 1 to {n} - The graph structure is fixed as described above - Adjacent vertices in paths are directly connected - Use responses wisely to navigate towards target Ready to start? Make your first query! Case D.60: C... | https://arxiv.org/abs/2505.17123v2 |
to win this game by strategically moving through the graph while following increasing or decreasing edge weights. Rules: 1. Game Setup: - Graph: {n}*{n}bipartite graph - Left nodes: {′,′.join(str(x)forxinrange (1, n+ 1))} - Right nodes: {′,′.join(str(x)forxinrange (n+ 1,2∗n+ 1))} - Edge weights: {chr(10).join(edge _des... | https://arxiv.org/abs/2505.17123v2 |
Turn Format: - Your move: “Breaking into: p1p2” - Example: “Breaking into: 10 7” Other Turns Format: - Your move: “Choosing: pBreaking into: p1p2” - My response: Either * “Choosing: xBreaking into: y z” or * “Choosing: xCannot break further” Example Round: Initial number: 13 You: “Breaking into: 10 7” - Breaking 13 int... | https://arxiv.org/abs/2505.17123v2 |
through strategic placement and overwriting. Success requires careful planning of number positions while adapting to new values each round. Case D.69: PaperNumbering Problem Template Let’s play the Paper Number Game! Rules: 1. Game Setup: -nblank papers in a line, numbered 1 to nfrom left to right - Game lasts {turns}r... | https://arxiv.org/abs/2505.17123v2 |
2” - Cell is adjacent to (2,2) Instructions: 1. Make choices based on grid values 2. Use exactly the format shown above 3. Explain your reasoning before each choice Remember: - Use exact format: “My Choice: x y” - Choose only adjacent cells after first turn - First move can be any cell - Keep track of both sums - Plan ... | https://arxiv.org/abs/2505.17123v2 |
- Available points: Point 1: ({x1},{y1}) Point 2: ({x2},{y2}) Point 3: ({x3},{y3}) Point 4: ({x4},{y4}) ... Point n: ({xn},{yn}) 2. Game Mechanics: - Players take turns choosing one point - Each point can only be chosen once - After each choice, add the squared distance to sum: * First turn: distance from ({sx},{sy})to... | https://arxiv.org/abs/2505.17123v2 |
vertex numbers for three bees Example Round: Initial placement: You: “My Choice: 1 2 3” - Placing bees at vertices 1,2,3 Me: “5” - Nastya appears at vertex 5 You: “My Choice: 2 3 4” - Moving bees to surround Nastya Me: “6” - Nastya moves to vertex 6 Result: You catch Nastya! Instructions: 1. Make moves based on graph s... | https://arxiv.org/abs/2505.17123v2 |
arXiv:2505.17126v1 [cs.CL] 21 May 2025Conformal Language Model Reasoning with Coherent Factuality Maxon Rubin-Toles1∗, Maya Gambhir1∗, Keshav Ramji1,2, Aaron Roth1, Surbhi Goel1 1University of Pennsylvania,2IBM Research AI ∗Denotes equal contribution May 26, 2025 Abstract Language models are increasingly being used in ... | https://arxiv.org/abs/2505.17126v1 |
faithful manner. This defini- tion requires that language model generations are both factual andcoherent by evaluating entire orderings of claims as correct rather than evaluating individual claims. An algorithm for coherent claim filtration. To apply this coherent definition of factuality, we propose a graph represent... | https://arxiv.org/abs/2505.17126v1 |
( α= 0.1). 2023. However, while these works are effective in their application domains, where claims may be treated as independent, they do not generalize to reasoning problems, where the correctness of each step cannot be evaluated without the context of the steps that precede it. LLM Reasoning. Chain-of-Thought (CoT)... | https://arxiv.org/abs/2505.17126v1 |
it is generally reasonable to assume that√ 2is irrational, it is not reasonable to do so in a proof of that fact. Background: Conformal prediction guarantees for LM generations. Mohri & Hashimoto 2024 improve the factuality of LM generations by splitting them into subclaims and filtering low- confidence subclaims via c... | https://arxiv.org/abs/2505.17126v1 |
fact). Remark 2. According to this definition, coherence cannot come at the cost of factuality. Deducibil- ity is only stricter than implication; in particular, any fact which is deducible from the ground truth must be implied by the ground truth. At worst, we might expect that by calibrating for this more stringent no... | https://arxiv.org/abs/2505.17126v1 |
algorithm for construction is in Appendix A). Remark 3. There may be multiple ideal deducibility graphs. For example, if a claim cis deducible from aorb, both deducible from vtrue, there is no way to represent this relationship uniquely without a hypergraph; a graph with edge (a, c)or(b, c)could be obtained by the algo... | https://arxiv.org/abs/2505.17126v1 |
L. While an approximate deducibility graph must exist, we further assume that we can construct one for each ( X, Y). In practice, we use GPT-4o to generate these graphs after splitting an output into claims, so we cannot enforce graph validity rigorously. However, our GPT-proxies satisfy Definition 4 in practice, which... | https://arxiv.org/abs/2505.17126v1 |
retention depends on our choice of claim-scoring function σ. We apply a context-independent claim-scoring function σindto score nodes individually. We refer to σindas self-consistency scoring. In practice, we compute σindas in Mohri & Hashimoto (2024) by querying GPT-4 to generate 5 alternate responses and counting the... | https://arxiv.org/abs/2505.17126v1 |
Yi)}n i=1, test point (Xn+1, Yn+1)∼ D, ground truth Ctrue, and desired error rate α. Then the following holds: 1−α≤P[Yˆqα n+1is coherently factual] . If, additionally, each Giis an approximate deducibility graph (see Definition 4) and r(X, Y,·)< ∞ ∀(X, Y)8, we have: P[Yˆqα n+1is coherently factual] ≤1−α+1 n+ 1. 5 Empir... | https://arxiv.org/abs/2505.17126v1 |
(shown in grey). In (b), we assess claim retention rates by varying αvalues, plotting both realized factuality and the fraction of retained claims across calibration methods and graph generation techniques. In (c) we plot claim retention with respect to user-desired calibration level. would be true if all its graph anc... | https://arxiv.org/abs/2505.17126v1 |
filtering for a realized factuality level, it is not calibrated to user input. For a fixed user-specified factuality rate (which post-hoc filtering will often overshoot), subgraph filtering achieves better claim retention than post-hoc filtering although post-hoc filtering shows potential to correct independently-calib... | https://arxiv.org/abs/2505.17126v1 |
approach to improve factuality of language models. Here we discuss limitations and potential future directions. Graph proxies. While our graph proxies satisfy the definition of deducibility graphs empirically, relying on a proprietary model like GPT-4o for accurate graph generation is not ideal. We note that GPT-4o str... | https://arxiv.org/abs/2505.17126v1 |
Isaac Gibbs, and Emmanuel J. Cand` es. Large language model validity via en- hanced conformal prediction methods, 2024. URL https://arxiv.org/abs/2406.09714 . Gianluca Detommaso, Martin Bertran, Riccardo Fogliato, and Aaron Roth. Multicalibration for confidence scoring in llms, 2024. URL https://arxiv.org/abs/2404.0468... | https://arxiv.org/abs/2505.17126v1 |
Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step, 2023. URL https://arxiv.org/abs/2305.20050 . Stephanie Lin, Jacob Hilton, and Owain Evans. Teaching models to express their uncertainty in words, 2022. URL https://arxiv.org/abs/2205.14334 . Terrance Liu and Zhi... | https://arxiv.org/abs/2505.17126v1 |
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowd- hery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models, 2023b. URL https://arxiv.org/abs/2203.11171 . Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi,... | https://arxiv.org/abs/2505.17126v1 |
Ctrue, S: Y → 2C. Generate the ideal graph Gideal= (Videal, Eideal)with Algorithm 2. Then, consider its subgraph onV:=Videal\L0=L1∪...∪Ln. Call this G= (V, E). Gis a DAG by construction, so to prove the approximate deducibility property, we fix some Gsubsatisfying ancestor connectedness. To show (1), assume that a cohe... | https://arxiv.org/abs/2505.17126v1 |
In particular, this means Vfiltered is a superset of Vbad. Note that Gn+1is an approximate deducibility graph and Ubadis an ancestor-connected sub- graph with no coherently factual ordering (if it had one, V′ badin particular would be coherently factual by Definition 4). Additionally, any superset of Vbadhas no coheren... | https://arxiv.org/abs/2505.17126v1 |
approximate deducibility graphs. G Further Related Work Factuality and Hallucination in Language Models. Ensuring the factuality of language model outputs is an important objective towards their reliable real-world deployment across diverse settings. Hallucinations can arise in several ways, including lack of knowledge... | https://arxiv.org/abs/2505.17126v1 |
seek to retain claims csuch that Ctrue=⇒cfor each c, and consider this sufficient for realizing factuality of an output. There is some difference in notation between this definition and the original since Mohri and Hashimoto frame the ground truth Ctrueas simply an element of Y, while we frame it as a set of claims. De... | https://arxiv.org/abs/2505.17126v1 |
1.0 was used to generate alternate responses for frequency scoring; a tem- perature of 0.0 was used for all other API calls. 2. GPT-4 was used for the generation of outputs for the MATH questions. 3. GPT-4 was used for self-consistency scoring, described in Section 4. 4. GPT-4o was used for graph generation. K.1 Depend... | https://arxiv.org/abs/2505.17126v1 |
this case, it would not be contributing to the complete output. By convention, we never include a claim in its own adjacency list (we do not consider a claim to rely on itself). Here, we’re interested in the dependency between claims, not just the correctness. For this reason, it’s also important to represent these dep... | https://arxiv.org/abs/2505.17126v1 |
subclaim jdepends on subclaim i. - A value of 0 indicates no dependency. - Ensure no claim depends on itself (diagonal entries must be 0). 3. Rules: - The adjacency list must be square, with nrows and ncolumns, where nis the exact number of subclaims provided. - Each row and column must be exactly nintegers. Do not inc... | https://arxiv.org/abs/2505.17126v1 |
fraction gives7 6= 14w. 28 Example 2 Question: Mr. Madoff invests 1000 dollars in a fund that compounds annually at a constant interest rate. After three years, his investment has grown to 1225 dollars. What is the annual interest rate, as a percentage? (Round your answer to the nearest integer.) Independent Factuality... | https://arxiv.org/abs/2505.17126v1 |
1. The left side of the equation is expanded to 2x2−6x+ 5x−15 = 14 2. The expanded equation simplifies to 2x2−x−29 = 0 3. Vieta’s formulas are used to find the sum of the roots of the equation 4. The sum of the roots of the equation is1 2 30 Example 5 Question: The points ( x, y) represented in this table lie on a stra... | https://arxiv.org/abs/2505.17126v1 |
20 women alight from the bus, then the number of women remaining on the bus is 54−20 = 34 women. Answer: 34 . Example 3 Question: John injured his back and needs to go to physical therapy. He went to physical therapy for 6 weeks. Each week he went twice for 2 hours at a time. If the sessions cost $125 per hour how much... | https://arxiv.org/abs/2505.17126v1 |
arXiv:2505.17131v1 [cs.CL] 22 May 2025Relative Bias: A Comparative Framework for Quantifying Bias in LLMs Alireza Arbabi Department of Computer Science University of Waterloo Waterloo, ON alireza.abrbabi@uwaterloo.ca Florian Kerschbaum Department of Computer Science University of Waterloo Waterloo, ON florian.kerschbau... | https://arxiv.org/abs/2505.17131v1 |
universal bias measurement method is inherently limited by the lack of a universally accepted and context-independent definition of bias itself. To address this issue, we propose a shift in perspective: rather than analyzing a single LLM in isolation, we suggest evaluating it in comparison to other models . By examinin... | https://arxiv.org/abs/2505.17131v1 |
focused on both detecting and 2 mitigating biases in LLMs, particularly on stereotypes or unequal treatment of marginalized groups [21,43,27,40,57,52]. The general methods that have been proposed can be categorized as: (1) Embedding-based methods analyze how identity-related and neutral concepts are positioned within t... | https://arxiv.org/abs/2505.17131v1 |
to generate those question with the aim of eliciting bias on the chosen LLMs. Prior research has explored the reliability and effectiveness of state-of-the-art LLMs in generating informative content when prompted with carefully constructed instructions [ 60,51], and several works have also demonstrated the utility of u... | https://arxiv.org/abs/2505.17131v1 |
an instruction- tuned embedding model that can generate task-aware embeddings. INSTRUCTOR is an embedding model which takes a text input besides a task instruction, and produce a vector embedding of the input with regards to the described task in the instruction. The instructions have a simple format of "Represent the ... | https://arxiv.org/abs/2505.17131v1 |
computations without requiring any fine-tuning or additional learning stages. Furthermore, This method represents one of the minimal complex computational approaches to textual analysis, as it relies solely on a single pass through an embedding model to convert each response into its vector representation. However, it ... | https://arxiv.org/abs/2505.17131v1 |
to undermine both the interpretability and consistency of the resulting evaluations. To address this problem, we design a fine-grained bias scoring rubric ranging from 1 to 10, with detailed descriptions for each score level to be used consistently across all experiments and bias domains (see Table 1 in the appendix). ... | https://arxiv.org/abs/2505.17131v1 |
controls the allowable range of deviation. Under the assumption that the distribution of baseline model means is approximately normal (which is held by assuming that the assigned bias scores are independent due to the Central-Limit-Theorem), kdefines the confidence level of acceptable variation. For example, k= 2corres... | https://arxiv.org/abs/2505.17131v1 |
higher bias scores across all categories compared to the baseline models. However, the AWS-hosted version of DeepSeek R1 does not show deviation from the other models, indicating a difference between the publicly released version and the one hosted on the DeepSeek website. Consequently, the statistical tests confirm th... | https://arxiv.org/abs/2505.17131v1 |
baseline model consensus, suggesting increased alignment, avoidance, or biased behavior of the model. 5 Discussion How alignments can introduce or remove bias, and how our framework can measure it. A key insight from our experiments is the observable behavioral difference between identical model architectures deployed ... | https://arxiv.org/abs/2505.17131v1 |
proposed framework has several limitations. First, it assesses bias only in a relative manner—its conclusions depend on comparing the target LLM’s behavior against a set of baseline models. As such, it does not make claims about the absolute level of bias in any single LLM. Second, the framework does not provide a comp... | https://arxiv.org/abs/2505.17131v1 |
language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) , pages 4171–4186, 2019. [14] Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei ... | https://arxiv.org/abs/2505.17131v1 |
Does prompt formatting have any impact on llm performance? arXiv preprint arXiv:2411.10541 , 2024. [29] Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300 , 2020. [30] Po-Sen Huang, Hu... | https://arxiv.org/abs/2505.17131v1 |
[48] Debora Nozza, Federico Bianchi, Dirk Hovy, et al. Honest: Measuring hurtful sentence completion in language models. In Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: Human language technologies . Association for Computational Linguistics, 2021. [4... | https://arxiv.org/abs/2505.17131v1 |
Schulman, Daniel Selsam, Kyla Sheppard, Toki Sherbakov, Jessica Shieh, Sarah Shoker, Pranav Shyam, Szymon Sidor, Eric Sigler, Maddie Simens, Jordan Sitkin, Katarina Slama, Ian Sohl, Benjamin Sokolowsky, Yang Song, Natalie 13 Staudacher, Felipe Petroski Such, Natalie Summers, Ilya Sutskever, Jie Tang, Nikolas Tezak, Mad... | https://arxiv.org/abs/2505.17131v1 |
and Björn Ross. A robust bias mitigation procedure based on the stereotype content model. arXiv preprint arXiv:2210.14552 , 2022. [66] Vellum AI. Llm leaderboard, 2025. URL https://www.vellum.ai/llm-leaderboard . Accessed: 2025-05-12. [67] Krzysztof Wach, Cong Doanh Duong, Joanna Ejdys, R ¯uta Kazlauskait ˙e, Pawel Kor... | https://arxiv.org/abs/2505.17131v1 |
Mykola Pechenizkiy. Gptbias: A comprehensive framework for evaluating bias in large language models. arXiv preprint arXiv:2312.06315 , 2023. [82] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-as-a-judge with mt-bench ... | https://arxiv.org/abs/2505.17131v1 |
(Figure 2) and GPT-4o are the same. A.3 Bias Criteria Table Score Description 1 Fully neutral and factual. Provides objective information with no emotive tone or ideological framing. Acknowledges all perspectives equally. 2 Mostly balanced and accurate, but shows slight preference in phrasing. Still includes both suppo... | https://arxiv.org/abs/2505.17131v1 |
SonnetDeepSeek AWS Jamba 1.5 Large Meta AI (Llama 4)Mistral Large123456789Bias Score DeepSeek R1 Cohere Command R+Llama 4 Maverick Claude 3.7 SonnetDeepSeek AWS Jamba 1.5 Large Meta AI (Llama 4)Mistral Large0246810Gemini 2.0 Flash judged scores across models Case Study 2: US-sensitive topics Figure 8: Box and violin pl... | https://arxiv.org/abs/2505.17131v1 |
AI (Llama 4)Mistral Large0.0240.0260.0280.0300.0320.0340.036Bias Score Embedding-based bias scores confidence intervals 95% Case Study 2: US-sensitive topics Figure 16: Confidence intervals (95%) for the embedding-based scores for Case Study 2: US-sensitive topics. DeepSeek R1 Cohere Command R+Llama 4 Maverick Claude 3... | https://arxiv.org/abs/2505.17131v1 |
t-statistic (Upper) -7.10 p-value (Lower) < 0.001 p-value (Upper) < 0.001 Equivalence Test Result Equivalent Conclusion Not Relatively Biased (Equivalent) 25 Case Study 2 (US): LLM-Judged (Gemini) Metric Value Target Model DeepSeek R1 Mean Bias (Target) 2.53 Mean Bias (Baseline) 2.45 Mean Difference 0.08 Equivalence Ma... | https://arxiv.org/abs/2505.17131v1 |
arXiv:2505.17132v1 [cs.CV] 22 May 2025Preprint ROBUSTIFYING VISION -LANGUAGE MODELS VIA DYNAMIC TOKEN REWEIGHTING Tanqiu Jiang†Jiacheng Liang†Rongyi Zhu†Jiawei Zhou†Fenglong Ma⋆Ting Wang† †Stony Brook University⋆Pennsylvania State University ABSTRACT Large vision-language models (VLMs) are highly vulnerable to jailbrea... | https://arxiv.org/abs/2505.17132v1 |
visual modality. Leveraging this formulation, D TRexamines the model’s KV cache to identify visual tokens that potentially cause safety-relevant shifts and se- lectively attenuates or eliminates their influence during inference. As illustrated in Figure 1, D TR 1DTR:Dynamic Token Reweighting. 1 Preprint Visual:Textual:... | https://arxiv.org/abs/2505.17132v1 |
Luo et al. (2024); Liu et al. (2024); Li et al. (2024). This work primarily focuses on defending VLMs against diverse multimodal jailbreak attacks in an attack-agnostic manner. Multimodal jailbreak defenses. Existing defenses against multimodal jailbreak attacks can be categorized as fine-tuning-stage or inference-stag... | https://arxiv.org/abs/2505.17132v1 |
‘ ∥’ denotes the concatenation operator. The attack aims to optimize xtxt,ximgsuch that the VLM’s response yprovides a meaningful answer to x. A variety of tactics can be employed, including i) pairing the harmful text prompt with an adversarial image, ii) combining a contextual image with seemingly harmless text to co... | https://arxiv.org/abs/2505.17132v1 |
al. (2025) and ESCO Gou et al. (2024) employ the victim model or another VLM to generate captions for ximg. However, this image-to-text conversion often incurs information loss (e.g., subtle jailbreak perturbations) critical for attack identification, while also introducing substantial runtime overhead (details in §5.2... | https://arxiv.org/abs/2505.17132v1 |
but has a negligible impact on benign queries; the second term quantifies the distance between the reweighted activation f(x(α))from the original activation f(x), which ensures the reweighting does not significantly distort the latent representations, thereby preserving the model’s general performance; the hyper-parame... | https://arxiv.org/abs/2505.17132v1 |
which is similar to ‘Recheck’ Liu et al. (2024) and ‘ASR-G’ Guo et al. (2024a) in previous studies. For utility preser- vation, we use the performance scores reported by the benchmarks. For inference efficiency, we measure the average inference time (AIT), defined as the average running time per benign query. Implement... | https://arxiv.org/abs/2505.17132v1 |
Utility preservation. Table 2 reports the 6 core vision-language capabilities (VLCs) of the llava-llama2-7b models defended by various methods on the MM-Vet benchmark. No- tably, thanks to its utility-preserving formulation (Eq. 7), D TRmaximally retains the benign- task performance of llava-llama2-7b : among the 6 VLC... | https://arxiv.org/abs/2505.17132v1 |
(e.g., “bomb” ) that semantically complete the jailbreak query; tokens with small αvalues (displayed in cold colors) correspond to adversarial noise that induce safety-relevant shifts. This explains how DTRmitigates the safety-relevant shift by reducing the weights of adversarial tokens. In comparison, when the same im... | https://arxiv.org/abs/2505.17132v1 |
manipulating token importance is challeng- ing, we approximate the adaptive attack as follows. We rank visual tokens in descending order based on their values in α∗and selectively nullify the weights of either the top or bottom p% (p= 20 or 50), representing varying allocations of reweighted tokens. 20% 50%Reweighting ... | https://arxiv.org/abs/2505.17132v1 |
Ma, and Jinghui Chen. Per- sonalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimization. In Proceedings of the Advances in Neural Information Processing Sys- tems (NeurIPS) , 2024b. Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, and Bao... | https://arxiv.org/abs/2505.17132v1 |
Yingshui Tan, Xiaoyong Zhu, Bo Zheng, and Xiangyu Yue. HiddenDetect: Detecting Jailbreak Attacks against Large Vision-Language Models via Monitor- ing Hidden States. ArXiv e-prints , 2025. Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. Alp... | https://arxiv.org/abs/2505.17132v1 |
Furong Huang, Alvaro Velasquez, Dinesh Manocha, and Amrit Singh Bedi. Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2025. Zhongwei Wan, Hui Shen, Xin Wang, Che Liu, Zheda Mai, and Mi... | https://arxiv.org/abs/2505.17132v1 |
activation vectors of the harmful and harmless sets. The refusal direction vector is computed once and cached for all subsequent experiments. At inference time, for each multimodal input, we optimize the scaling vector αfor visual tokens following Eq. 7. We use the AdamW optimizer with a learning rate of 0.01 and run f... | https://arxiv.org/abs/2505.17132v1 |
λ Table 7 breaks down the impact of λon D TR’s attack robustness and utility retention in individual tasks on the MM-Vet benchmark. Table 6: Task performance comparison between LLaVA-v1.5-7b baseline and LLaVA-v1.5-7b + D TRon the MME benchmark. Task Base DTR Task Base DTR Perception Cognition existence 190.00 190.00 c... | https://arxiv.org/abs/2505.17132v1 |
37.7 48.5 42.2 43.8 38.3 0.1 9.21 30.7 27.7 38.2 49.8 40.4 44.4 38.5 0.5 11.84 32.6 25.0 41.1 49.3 39.9 42.2 38.3 1.0 15.79 30.6 23.8 39.1 50.3 40.7 44.4 38.1 2.0 19.74 31.0 23.5 39.1 49.7 41.6 45.1 38.3 5.0 22.37 30.7 27.7 38.2 49.8 40.4 44.4 38.5 10.0 26.32 30.7 29.6 39.4 49.6 41.3 43.6 39.0 100.0 25.00 32.9 30.4 41.... | https://arxiv.org/abs/2505.17132v1 |
LongMagpie: A Self-synthesis Method for Generating Large-scale Long-context Instructions Chaochen Gao1,2, Xing Wu1,2,3, Zijia Lin4, Debing Zhang3, Songlin Hu1,2 1Institute of Information Engineering, Chinese Academy of Sciences 2School of Cyber Security, University of Chinese Academy of Sciences 3Xiaohongshu Inc,4Tsing... | https://arxiv.org/abs/2505.17134v1 |
(Xu et al., 2024c), has gained widespread attention for eliminating the need for seed instructions and prompt engineering required by previous approaches (Wang et al., 2022a; Li et al., 2025; Xu et al., 2024b; Bai et al., 2024a). It creates alignment data by prompting aligned LLMs with only special tokens preceding a u... | https://arxiv.org/abs/2505.17134v1 |
effectively balances the model’s performance on both long-context and short-context tasks. •We conduct extensive evaluations demonstrating that models trained on LongMagpie-generated data achieve leading results on long-context benchmarks compared to existing methods. •We provide in-depth analyses revealing the key fac... | https://arxiv.org/abs/2505.17134v1 |
document, query, and tokens that precede an assistant response (e.g., <|eot_id|><|start_header_id|>assistant<|end_header_id|> for Llama-3-Instruct). We then generate a response R, forming a complete instruction triplet (D, Q, R )for long-context training. If the same model is used for both query and response generation... | https://arxiv.org/abs/2505.17134v1 |
sources, including LongAlpaca12k (Chen et al., 2023b) and GPT-4 samples from Open Orca (Lian et al., 2023), containing 1.5 million synthetic instructions. In this work, we refer to ChatQA2 as ChatQA by default; LongAlign (Bai et al., 2024a) generates questions and answers for long documents by prompting LLMs. •Short In... | https://arxiv.org/abs/2505.17134v1 |
long-context evaluation, topping HELMET (62.10), RULER (91.17), LongBench-v2 (34.4) and the LongA VG score (62.56) within the Long Instruction Data group. The performance gains are substantial compared to existing long-context instruction datasets: LongMagpie outperforms ChatQA by +1.87 on HELMET, +1.35 on RULER, and +... | https://arxiv.org/abs/2505.17134v1 |
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