19 citations · 38 across the 9 of their papers we have counts for
9 papers
Inverse-Q*: Token Level Reinforcement Learning for Aligning Large Language Models Without Preference Data
Han Xia, Songyang Gao, Qiming Ge +3
Reinforcement Learning from Human Feedback (RLHF) has proven effective in aligning large language models with human intentions, yet it often relies on complex methodologies like Pr…
EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models
Weikang Zhou, Xiao Wang, Limao Xiong +18
Jailbreak attacks are crucial for identifying and mitigating the security vulnerabilities of Large Language Models (LLMs). They are designed to bypass safeguards and elicit prohibi…
Navigating the OverKill in Large Language Models
Chenyu Shi, Xiao Wang, Qiming Ge +7
Large language models are meticulously aligned to be both helpful and harmless. However, recent research points to a potential overkill which means models may refuse to answer beni…
Secrets of RLHF in Large Language Models Part II: Reward Modeling
Binghai Wang, Rui Zheng, Lu Chen +24
Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more hel…
RealBehavior: A Framework for Faithfully Characterizing Foundation Models' Human-like Behavior Mechanisms
Enyu Zhou, Rui Zheng, Zhiheng Xi +7
Reports of human-like behaviors in foundation models are growing, with psychological theories providing enduring tools to investigate these behaviors. However, current research ten…
TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models
Xiao Wang, Yuansen Zhang, Tianze Chen +9
Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. However, the continual learning aspect of th…