3 citations · 3 across the 2 of their papers we have counts for
5 papers
Fixing the Broken Compass: Diagnosing and Improving Inference-Time Reward Modeling
Jiachun Li, Pengfei Cao, Zhuoran Jin +6
Inference-time scaling techniques have shown promise in enhancing the reasoning capabilities of large language models (LLMs). While recent research has primarily focused on trainin…
LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning
Jiachun Li, Pengfei Cao, Chenhao Wang +6
Large language models (LLMs) sometimes demonstrate poor performance on knowledge-intensive tasks, commonsense reasoning is one of them. Researchers typically address these issues b…
AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation
Zhitao He, Pengfei Cao, Chenhao Wang +7
With the development of deep learning, natural language processing technology has effectively improved the efficiency of various aspects of the traditional judicial industry. Howev…
Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models
Zhuoran Jin, Pengfei Cao, Hongbang Yuan +6
Recently, retrieval augmentation and tool augmentation have demonstrated a remarkable capability to expand the internal memory boundaries of language models (LMs) by providing exte…
Tug-of-War Between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models
Zhuoran Jin, Pengfei Cao, Yubo Chen +5
Retrieval-augmented language models (RALMs) have demonstrated significant potential in refining and expanding their internal memory by retrieving evidence from external sources. Ho…