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CitaLaw: Enhancing LLM with Citations in Legal Domain

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arxiv 2412.14556 v2 pith:V6AH6KCR submitted 2024-12-19 cs.CL

classification cs.CL
keywords citationslegalcitalawresponsescorpusevaluationllmsquestions
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we propose CitaLaw, the first benchmark designed to evaluate LLMs' ability to produce legally sound responses with appropriate citations. CitaLaw features a diverse set of legal questions for both laypersons and practitioners, paired with a comprehensive corpus of law articles and precedent cases as a reference pool. This framework enables LLM-based systems to retrieve supporting citations from the reference corpus and align these citations with the corresponding sentences in their responses. Moreover, we introduce syllogism-inspired evaluation methods to assess the legal alignment between retrieved references and LLM-generated responses, as well as their consistency with user questions. Extensive experiments on 2 open-domain and 7 legal-specific LLMs demonstrate that integrating legal references substantially enhances response quality. Furthermore, our proposed syllogism-based evaluation method exhibits strong agreement with human judgments.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Missing Link: Joint Legal Citation Prediction using Heterogeneous Graph Enrichment

    cs.SI 2025-06 accept novelty 6.0 of 10

    A graph neural network that enriches legal citation graphs with categorical metadata nodes predicts case and law citations more accurately than prior GNN baselines, and joint training boosts case citation prediction.

  2. LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents

    cs.AI 2025-09 reject novelty 5.0 of 10

    On CUAD legal contracts, a prompt-engineered QWEN-2 pipeline with chunking and two answer-selection heuristics reportedly outperforms the fine-tuned DeBERTa-large baseline by about 9%, reaching claimed state-of-the-ar...

  3. Bridging Search and Recommendation through Latent Cross Reasoning

    cs.IR 2025-08 conditional novelty 5.0 of 10

    A latent cross reasoning model with contrastive learning and GRPO reinforcement learning improves search-enhanced recommendation on Qilin and KuaiSAR.

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