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20232026
most citedSafe RLHF: Safe Reinforcement Learning from Human Feedback

21 citations · 23 across the 21 of their papers we have counts for

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12 papers · 1 filter

cs.CL2026

SPADE-Bench: Evaluating Spontaneous Strategic Deception in Agents via Plan-Action Divergence

Yuyan Bu, Haowei Li, Qirui Zheng +7

As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment. However, in practical applications, human users cannot monitor eve…

cs.CL2026

Align Once, Benefit Multilingually: Enforcing Multilingual Consistency for LLM Safety Alignment

Yuyan Bu, Xiaohao Liu, ZhaoXing Ren +2

The widespread deployment of large language models (LLMs) across linguistic communities necessitates reliable multilingual safety alignment. However, recent efforts to extend align…

cs.CL2025

LLMs Know More Than Words: A Genre Study with Syntax, Metaphor & Phonetics

Weiye Shi, Zhaowei Zhang, Shaoheng Yan +1

Large language models (LLMs) demonstrate remarkable potential across diverse language related tasks, yet whether they capture deeper linguistic properties, such as syntactic struct…

cs.CL2025

SafeMT: Multi-turn Safety for Multimodal Language Models

Han Zhu, Juntao Dai, Jiaming Ji +8

With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are more common in everyday interacti…

cs.CL2025

SafeLawBench: Towards Safe Alignment of Large Language Models

Chuxue Cao, Han Zhu, Jiaming Ji +7

With the growing prevalence of large language models (LLMs), the safety of LLMs has raised significant concerns. However, there is still a lack of definitive standards for evaluati…

cs.CL2025

Benchmarking Multi-National Value Alignment for Large Language Models

Weijie Shi, Chengyi Ju, Chengzhong Liu +8

Do Large Language Models (LLMs) hold positions that conflict with your country's values? Occasionally they do! However, existing works primarily focus on ethical reviews, failing t…