4 papers
AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning
Tzu-Han Lin, Wei-Lin Chen, Chen-An Li +3
Equipping large language models (LLMs) with search engines via reinforcement learning (RL) promises effective search agents. However, adaptively balancing internal parametric knowl…
Transferring Textual Preferences to Vision-Language Understanding through Model Merging
Chen-An Li, Tzu-Han Lin, Yun-Nung Chen +1
Large vision-language models (LVLMs) perform outstandingly across various multimodal tasks. However, their ability to evaluate generated content remains limited, and training visio…
DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging
Tzu-Han Lin, Chen-An Li, Hung-yi Lee +1
Reinforcement learning from human feedback (RLHF) is a popular strategy for aligning large language models (LLMs) with desired behaviors. Reward modeling is a crucial step in RLHF.…
Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models
Cheng-Hsun Hsueh, Paul Kuo-Ming Huang, Tzu-Han Lin +4
Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters. However, recent studies ha…