2 citations · 2 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…
JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning
Huanghai Liu, Quzhe Huang, Qingjing Chen +5
In recent years, Large Language Models (LLMs) have been widely applied to legal tasks. To enhance their understanding of legal texts and improve reasoning accuracy, a promising app…
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…
Only One Relation Possible? Modeling the Ambiguity in Event Temporal Relation Extraction
Yutong Hu, Quzhe Huang, Yansong Feng
Event Temporal Relation Extraction (ETRE) aims to identify the temporal relationship between two events, which plays an important role in natural language understanding. Most previ…
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…