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cs.CL2026
Beyond Recall Saturation: Scaling Hard-Negative Reranker Training for Industrial Legal Retrieval
Yunhan Li, Mingjie Xie, Zeyang Shi +2
Industrial retrieval systems often improve quality by enlarging embedding candidate pools or adopting complex training pipelines. We show that, under a sufficiently large but pract…
cs.CL2026
LegalCiteTrust: Benchmarking Citation Trustworthiness in Chinese Long-Form Legal Research Reports
Yunhan Li, Mingjie Xie, Zeyang Shi +2
Long-form legal research reports increasingly rely on LLMs and agentic research systems, but their reliability depends not only on answering the task, but also on whether cited leg…
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…