2 citations · 4 across the 5 of their papers we have counts for
5 papers
DPlan: Dual-Agent Dynamic Global Planning for Complex Retrieval-Augmented Reasoning
Kangcheng Luo, Tinglang Wu, Yansong Feng
Recent search-augmented LLMs trained with reinforcement learning (RL) can interleave searching and reasoning for multi-hop reasoning tasks. However, they face two critical failure…
Automating Legal Interpretation with LLMs: Retrieval, Generation, and Evaluation
Kangcheng Luo, Quzhe Huang, Cong Jiang +1
Interpreting the law is always essential for the law to adapt to the ever-changing society. It is a critical and challenging task even for legal practitioners, as it requires metic…
ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal Advice
Yutong Hu, Kangcheng Luo, Yansong Feng
Despite remarkable performance in legal consultation exhibited by legal Large Language Models(LLMs) combined with legal article retrieval components, there are still cases when the…
What Kinds of Tokens Benefit from Distant Text? An Analysis on Long Context Language Modeling
Yutong Hu, Quzhe Huang, Kangcheng Luo +1
As the context length that large language models can handle continues to increase, these models demonstrate an enhanced ability to utilize distant information for tasks such as lan…
PACE: Improving Prompt with Actor-Critic Editing for Large Language Model
Yihong Dong, Kangcheng Luo, Xue Jiang +2
Large language models (LLMs) have showcased remarkable potential across various tasks by conditioning on prompts. However, the quality of different human-written prompts leads to s…