Language Model Safety Evaluation

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45 papers in the last four weeks, down 15% on the four weeks before. 0.3% of all new papers.

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Please refuse to answer me! Mitigating Over-Refusal in Large Language Models via Adaptive Contrastive Decoding
Apr 18, 2026Yupeng Qi, Ziyu Lyu, Lixin Cui +2Language Model Safety Evaluation LLM Refusal Behavior
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University · School of Information, Central University of Finance and Economics · School of Artificial Intelligence, Beijing Normal University +1
Do LLMs Hold Their Values? MANTA: A Multi-Turn Adversarial Benchmark for Animal Welfare Reasoning
Apr 18, 2026Isabella Luong, Joyee Chen, Arturs Kanepajs +5LLM Evaluation Moral Reasoning in Language Models
SPAR · Compassion Aligned Machine Learning · NUS +2
Jailbreaking Large Language Models with Morality Attacks
Apr 18, 2026Ying Su, Mingen Zheng, Weili Diao +1Moral Reasoning in Language Models Language Model Safety Evaluation
South China University of Technology1 · HKUST2 · Beihang University3
Evaluating Multimodal LLMs for Inpatient Diagnosis: Real-World Performance, Safety, and Cost Across Ten Frontier Models
Apr 18, 2026Bruce A. Bassett, Amy Rouillard, Sitwala Mundia +8Language Model Safety Evaluation Healthcare
School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa · Wits MIND Institute, University of the Witwatersrand, Johannesburg, South Africa · Grai Labs, Cape Town, South Africa +3
SafeDream: Safety World Model for Proactive Early Jailbreak Detection
Apr 18, 2026Bo Yan, Weikai Lin, Yada Zhu +1Language Model Safety Evaluation World Models
University of Central Florida · University of Rochester · IBM Research
Benign Fine-Tuning Breaks Safety Alignment in Audio LLMs
Apr 17, 2026Jaechul Roh, Virat Shejwalkar, Amir HoumansadrLanguage Model Safety Evaluation LLM Fine-Tuning
University of Massachusetts Amherst
MCBench: A Multicontext Safety Assessment Benchmark for Omni Large Language Models
Apr 17, 2026Manh Luong, Tamas Abraham, Junae Kim +6LLM Safety Benchmarks Language Model Safety Evaluation
Monash University, Australia · Defence Science and Technology Group, Australia
TWGuard: A Case Study of LLM Safety Guardrails for Localized Linguistic Contexts
Apr 17, 2026Hua-Rong Chu, Kuan-Chun Wang, Yao-Te HuangLanguage Model Safety Evaluation LLM Guardrails
Chunghwa Telecom Laboratories
Context Over Content: Exposing Evaluation Faking in Automated Judges
Apr 16, 2026Manan Gupta, Inderjeet Nair, Lu Wang +1LLM-as-a-Judge Language Model Safety Evaluation
1BITS Pilani, India · University of Michigan
VoxSafeBench: Not Just What Is Said, but Who, How, and Where
Apr 16, 2026Yuxiang Wang, Hongyu Liu, Yijiang Xu +9Language Model Safety Evaluation Privacy Leakage in Language Models
1The Chinese University of Hong Kong, Shenzhen · 2Shenzhen Loop Area Institute · 3Amphion Technology Co., Ltd.
Kill-Chain Canaries: Stage-Level Tracking of Prompt Injection Across Attack Surfaces and Five Production LLMs
Mar 30, 2026Haochuan Kevin Wang, Zechen ZhangMulti-Agent LLM Systems Language Model Safety Evaluation
Massachusetts Institute of Technology · University of Chicago
How Utilitarian Are OpenAI's Models Really? Replicating and Reinterpreting Pfeffer, Krügel, and Uhl (2025)
Mar 24, 2026Johannes HimmelreichPrompt Sensitivity Moral Reasoning in Language Models
The Hidden Puppet Master: Predicting Human Belief Change in Manipulative LLM Dialogues
Mar 21, 2026Jocelyn Shen, Amina Luvsanchultem, Jessica Kim +6Opinion Dynamics Language Model Safety Evaluation
Massachusetts Institute of Technology, Cambridge, MA, USA · Carnegie Mellon University, Pittsburgh, PA, USA
Risk-Adjusted Harm Scoring for Automated Red Teaming for LLMs in Financial Services
Mar 11, 2026Fabrizio Dimino, Bhaskarjit Sarmah, Stefano PasqualiLanguage Model Safety Evaluation LLM Safety
1Domyn, New York, US · 2Domyn, New York, US · 3Domyn, New York, US
AdaCultureSafe: Adaptive Cultural Safety Grounded by Cultural Knowledge in Large Language Models
Mar 9, 2026Hankun Kang, Di Lin, Zhirong Liao +6Language Model Safety Evaluation Cultural Knowledge in Language Models
School of Computer Science, Wuhan University, China · Tsinghua University, China · Jiangxi University of Finance and Economics, China
MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
Mar 3, 2026Zhongxi Wang, Yueqian Lin, Jingyang Zhang +3LLM Safety Benchmarks Language Model Safety Evaluation
1Duke University · 2Virtue AI
NEST: Nascent Encoded Steganographic Thoughts
Feb 15, 2026Artem KarpovLanguage Model Safety Evaluation LLM Auditing
Independent.
AI Chatbot Suicide Risk Detection and Response: Human Validation Study of the Open-Source VERA-MH Safety Evaluation
Feb 4, 2026Kate H. Bentley, Luca Belli, Adam M. Chekroud +7Language Model Safety Evaluation Human-AI Agreement
Spring Health · UC Berkeley · Yale University
CoT is Not the Chain of Truth: An Empirical Internal Analysis of Reasoning LLMs for Fake News Generation
Feb 4, 2026Zhao Tong, Chunlin Gong, Yiping Zhang +5Language Model Safety Evaluation LLM Interpretability
Institute of Information Engineering, Chinese Academy of Sciences · School of Cyber Security, University of Chinese Academy of Sciences · Shanghai AI Laboratory +3
SalamahBench: Dialect and Category Level Safety Evaluation of Arabic Language Models
Feb 3, 2026Omar Abdelnasser, Fatemah Alharbi, Khaled Khasawneh +2LLM Safety Benchmarks Multilingual Language Model Evaluation
Compumacy for Artificial Intelligence Solutions, Cairo, Egypt · Computer Science Department, College of Computer Science and Engineering, Taibah University, Yanbu 46522, KSA · Electrical and Computer Engineering Department, George Mason University, VA, USA +2
VoxPrivacy: A Benchmark for Evaluating Interactional Privacy of Speech Language Models
Jan 27, 2026Yuxiang Wang, Hongyu Liu, Dekun Chen +2Privacy-Preserving Language Models Language Model Safety Evaluation
The Chinese University of Hong Kong, Shenzhen · Shenzhen Loop Area Institute · Amphion Technology Co., Ltd. +1
Unknown Unknowns: Do Hidden Intentions in LLMs Evade Detection?
Jan 26, 2026Devansh Srivastav, David Pape, Lea SchönherrLanguage Model Safety Evaluation LLM Auditing
CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
Health-ORSC-Bench: A Benchmark for Measuring Over-Refusal and Safety Completion in Health Context
Jan 25, 2026Zhihao Zhang, Liting Huang, Guanghao Wu +3LLM Safety Benchmarks Language Model Safety Evaluation
Macquarie University1 · UTS2 · MBZUAI3 +1
SycoEval-EM: Sycophancy Evaluation of Large Language Models in Simulated Clinical Encounters for Emergency Care
Jan 23, 2026Dongshen Peng, Yi Wang, Austin Schoeffler +5LLM Sycophancy Language Model Safety Evaluation
UNC Chapel Hill · University of Waterloo · Stanford University
In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement
Jan 19, 2026Anudeex Shetty, Aditya Joshi, Salil S. KanhereLanguage Model Safety Evaluation Adversarial Attacks on LLMs
School of Computer Science and Engineering, UNSW Sydney, Australia · School of Computing and Information System, the University of Melbourne, Australia
Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection
Jan 17, 2026Muhammad Alif Al Hakim, Alfan Farizki Wicaksono, Fajri KotoLLM Quantization Multilingual Language Model Evaluation
Faculty of Computer Science, Universitas Indonesia · Mohamed bin Zayed University of Artificial Intelligence
Towards Multi-modal Multi-turn Safety: From Agentic Interaction to Strategic Alignment
Jan 8, 2026Han Zhu, Jiale Chen, Chengkun Cai +8Language Model Safety Evaluation Multimodal Large Language Models
Hong Kong University of Science and Technology · University College London · AISpeech +1
Safety Is Not Universal: The Selective Safety Trap in LLM Alignment
Jan 7, 2026Iago Alves Brito, Walcy Santos Rezende Rios, Julia Soares Dollis +2LLM Safety Benchmarks LLM Alignment
Advanced Knowledge Center for Immersive Technologies (AKCIT) Federal University of Goiás (UFG)
Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs
Jan 5, 2026Amirali Ebrahimzadeh, Seyyed M. SaliliLong-Context Retrieval LLM Hallucination Mitigation
Department of Electrical Engineering & Computer Science, University of Michigan, Ann Arbor, MI 48109, USA · Independent Researcher, Ann Arbor, MI 48106, USA
Training-Free Policy Violation Detection via Activation-Space Whitening in LLMs
Dec 3, 2025Oren Rachmil, Avishag Shapira, Roy Betser +5Language Model Safety Evaluation LLM Auditing
Fujitsu Research of Europe · Ben-Gurion University of the Negev

Apr 18, 2026, cs.CLJ/K move · Enter open · S save

Please refuse to answer me! Mitigating Over-Refusal in Large Language Models via Adaptive Contrastive Decoding

Yupeng Qi, Ziyu Lyu, Lixin Cui, Lu Bai+1

School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University · School of Information, Central University of Finance and Economics · School of Artificial Intelligence, Beijing Normal University+1

Language Model Safety Evaluation LLM Refusal Behavior LLM Safety Over-Refusal Contrastive Decoding Refusal Behavior in Language Models

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Safety-aligned large language models (LLMs) often generate refusal responses to harmless queries due to the over-refusal problem. However, existing methods for mitigating over-refusal cannot maintain a low refusal ratio for harmless queries while keeping a high refusal ratio for malicious ones. In this paper, we analyze how system prompts with varying safety levels affect LLM refusal behaviors when facing over-refusal queries. A key observation is that, when LLMs suffer from the over-refusal issue, non-refusal tokens remain present in the next-token candidate list, but the model systematically fails to select them, despite the generation of refusal tokens. Based on this observation, we propose a training-free and model-agnostic approach, Adaptive Contrastive Decoding (AdaCD), to mitigate over-refusal while maintaining LLM safety. First, AdaCD compares the output distributions of the LLM with or without an extreme safety system prompt to refine the refusal token distribution. Second, we introduce an adaptive contrastive decoding strategy that dynamically incorporates or removes the refusal token distribution, adaptively boosting the probability of selecting refusal or non-refusal tokens. Experimental results on five benchmark datasets show that, on average, AdaCD reduces the refusal ratio for over-refusal queries by 10.35%, yet still increases the refusal ratio for malicious queries by 0.13%. Code is available at https://github.com/OutdoorManofML/AdaCD.

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