3 papers
cs.IR2026
Optimizing Retrieval Components for a Shared Backbone via Component-Wise Multi-Stage Training
Yunhan Li, Mingjie Xie, Zihan Gong +3
Recent advances in embedding-based retrieval have enabled dense retrievers to serve as core infrastructure in many industrial systems, where a single retrieval backbone is often sh…
cs.IR2026
LegalMALR:Multi-Agent Query Understanding and LLM-Based Reranking for Chinese Statute Retrieval
Yunhan Li, Mingjie Xie, Gaoli Kang +3
Statute retrieval is essential for legal assistance and judicial decision support, yet real-world legal queries are often implicit, multi-issue, and expressed in colloquial or unde…
cs.CL2025
LegalEval-Q: A New Benchmark for The Quality Evaluation of LLM-Generated Legal Text
Li yunhan, Wu gengshen
As large language models (LLMs) are increasingly used in legal applications, current evaluation benchmarks tend to focus mainly on factual accuracy while largely neglecting importa…