4 papers
Value of Information: A Framework for Human-Agent Communication
Yijiang River Dong, Tiancheng Hu, Zheng Hui +4
Large Language Model (LLM) agents deployed for real-world tasks face a fundamental dilemma: user requests are underspecified, yet agents must decide whether to act on incomplete in…
Steer Model beyond Assistant: Controlling System Prompt Strength via Contrastive Decoding
Yijiang River Dong, Tiancheng Hu, Zheng Hui +1
Large language models excel at complex instructions yet struggle to deviate from their helpful assistant persona, as post-training instills strong priors that resist conflicting in…
Privacy-R1: Privacy-Aware Multi-LLM Agent Collaboration via Reinforcement Learning
Zheng Hui, Yijiang River Dong, Sanhanat Sivapiromrat +2
When users submit queries to Large Language Models (LLMs), their prompts can often contain sensitive data, forcing a difficult choice: Send the query to a powerful proprietary LLM…
When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning
Yijiang River Dong, Tiancheng Hu, Yinhong Liu +2
While Reinforcement Learning from Human Feedback (RLHF) is widely used to align Large Language Models (LLMs) with human preferences, it typically assumes homogeneous preferences ac…