Technology Category
Natural Language Processing: Question AnsweringMultiagent Systems: Modeling other AgentsData Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation
🤖 AI Summary
This study addresses the challenges of legal consultation QA, including scarce high-quality data, task complexity, and strong contextual dependencies. The authors propose a structured task decomposition approach grounded in a legal element graph, modeling queries as graph structures that integrate entities, events, intents, and legal issues. They introduce JurisMA, a modular and interpretable multi-agent reasoning framework incorporating dynamic routing, statute grounding, and style optimization mechanisms. Trained on JurisCQAD—a large-scale, self-constructed Chinese legal consultation dataset—the system significantly outperforms both general-purpose and legal-domain large language models across multiple semantic and lexical metrics on the LawBench benchmark.
Technology Category
Application Category
📝 Abstract
Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks, including the scarcity of high-quality training data, complex task composition, and strong contextual dependencies. To address these, we construct JurisCQAD, a large-scale dataset of over 43,000 real-world Chinese legal queries annotated with expert-validated positive and negative responses, and design a structured task decomposition that converts each query into a legal element graph integrating entities, events, intents, and legal issues. We further propose JurisMA, a modular multi-agent framework supporting dynamic routing, statutory grounding, and stylistic optimization. Combined with the element graph, the framework enables strong context-aware reasoning, effectively capturing dependencies across legal facts, norms, and procedural logic. Trained on JurisCQAD and evaluated on a refined LawBench, our system significantly outperforms both general-purpose and legal-domain LLMs across multiple lexical and semantic metrics, demonstrating the benefits of interpretable decomposition and modular collaboration in Legal CQA.
Problem
Research questions and friction points this paper is trying to address.
Legal consultation question answering
data scarcity
task complexity
contextual dependency
Innovation
Methods, ideas, or system contributions that make the work stand out.
legal consultation
multi-agent framework
structured reasoning
legal element graph
dataset construction
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