Multi-agent LLM framework simulates Chinese civil trials through five-stage procedures with memory and retrieval, producing judgments strong in liability allocation and multi-item decisions.
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LexRubric is a rubric-based benchmark containing 649 instances and 12,337 atomic criteria for diagnostic evaluation of LLMs on open-ended Chinese legal consultation and judicial examination tasks across 14 scenarios.
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
LLMs consistently overrate relevance of inadequate passages in IR evaluations due to biases toward length and lexical features rather than true content match.
ChunkGroupSHAP clusters semantically related chunks into shared cross-document features for listwise Shapley explanations of embedding-based rankings.
citing papers explorer
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Civil Court Simulation with Large Language Models
Multi-agent LLM framework simulates Chinese civil trials through five-stage procedures with memory and retrieval, producing judgments strong in liability allocation and multi-item decisions.
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LexRubric: A Rubric-Guided Diagnostic Benchmark for Open-Ended Legal Tasks
LexRubric is a rubric-based benchmark containing 649 instances and 12,337 atomic criteria for diagnostic evaluation of LLMs on open-ended Chinese legal consultation and judicial examination tasks across 14 scenarios.
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Formalized Information Needs Improve Large-Language-Model Relevance Judgments
Synthetically formalizing information needs into topics with descriptions and narratives improves LLM relevance assessor agreement with humans and reduces over-labeling of relevant documents on TREC Deep Learning and Robust04.
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When LLM Judges Inflate Scores: Exploring Overrating in Relevance Assessment
LLMs consistently overrate relevance of inadequate passages in IR evaluations due to biases toward length and lexical features rather than true content match.
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Listwise Explanation of Embedding-Based Rankings via Semantic Chunk Grouping
ChunkGroupSHAP clusters semantically related chunks into shared cross-document features for listwise Shapley explanations of embedding-based rankings.