AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
[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 View PDF HTML (experimental) 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) Full-text links: Access Paper: 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 View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs cs.IR References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)