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Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy
Chris Yuhao Liu, Liang Zeng, Yuzhen Xiao +9
Despite the critical role of reward models (RMs) in Reinforcement Learning from Human Feedback (RLHF), current state-of-the-art open RMs perform poorly on most existing evaluation…
DRAGON: Guard LLM Unlearning in Context via Negative Detection and Reasoning
Yaxuan Wang, Chris Yuhao Liu, Quan Liu +4
Unlearning in Large Language Models (LLMs) is crucial for protecting private data and removing harmful knowledge. Most existing approaches rely on fine-tuning to balance unlearning…
GUARD: Generation-time LLM Unlearning via Adaptive Restriction and Detection
Zhijie Deng, Chris Yuhao Liu, Zirui Pang +5
Large Language Models (LLMs) have demonstrated strong capabilities in memorizing vast amounts of knowledge across diverse domains. However, the ability to selectively forget specif…
Large Language Model Unlearning via Embedding-Corrupted Prompts
Chris Yuhao Liu, Yaxuan Wang, Jeffrey Flanigan +1
Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a large language model should not know is important for ens…
LLM Unlearning via Loss Adjustment with Only Forget Data
Yaxuan Wang, Jiaheng Wei, Chris Yuhao Liu +6
Unlearning in Large Language Models (LLMs) is essential for ensuring ethical and responsible AI use, especially in addressing privacy leak, bias, safety, and evolving regulations.…