[Submitted on 22 Sep 2026]
Title:LEGO: Synergizing Expert GraphRAG and Expert Chain-of-Thought for Legal Reasoning
View a PDF of the paper titled LEGO: Synergizing Expert GraphRAG and Expert Chain-of-Thought for Legal Reasoning, by Qingjing Chen and Junkai Zhang and Shaochun Wang and Jiahao Ding and Siyuan Zheng and Yukun Yan and Zhi Zheng and Antonino Rotolo and Yun Liu and Weixing Shen
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Abstract:Large language models are increasingly applied to high-risk domains such as law, yet complex legal reasoning remains limited by two structural challenges. First, existing RAG and GraphRAG methods emphasize lexical or semantic similarity while overlooking normative relations among legal provisions. Second, vanilla Chain-of-Thought prompting may generate plausible rationales without enforcing the normative structure of legal reasoning. To deal with the bottleneck of pipelines in the legal reasoning domain, we propose LEGO, a dual-module framework that synergizes Legal Expert GraphRAG and expert Chain-of-thought for complex legal reasoning. ExpertGraphRAG uses an expert-annotated civil code graph encoding these normative relations with a greedy normative-coverage retrieval algorithm to dynamically extract instance-specific provision subgraphs, while ExpertCoT organizes the retrieved provisions and case facts into structured Provision-Fact-Conclusion reasoning. With a Qwen3-8B backbone, LEGO achieves 40.53% exact-match accuracy on LawExamQA_Civil, outperforming the evaluated RAG and CoT baselines and performing comparably to the evaluated larger models, while remaining robust on multi-hop questions. It also achieves the best results among the evaluated baselines on the open-ended benchmarks. Ablation studies confirm the individual and complementary contributions of both modules, demonstrating LEGO's effectiveness in improving LLMs' complex legal reasoning ability. Code and dataset can be found in the link: this https URL
Comments: Accepted to EMNLP 2026(Findings)
Subjects:
Computation and Language (cs.CL); Information Retrieval (cs.IR)
Cite as: arXiv:2609.27009 [cs.CL]
(or arXiv:2609.27009v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.27009
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Qingjing Chen [view email] [v1] Tue, 22 Sep 2026 19:48:52 UTC (19,490 KB)
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