From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation
Year: 2026
Type: conference-paper
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 JU-RISMA, 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.
Authors: Mingfei Lu, Yi Zhang, Mengjia Wu, Yue Feng
Institutions: University of Technology Sydney, University of Birmingham
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166807025