title: LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements
    Generation
summary: Legal Case Retrieval (LCR), which retrieves relevant cases from a query case,
  is a fundamental task for legal professionals in research and decision-making.
  However, existing studies on LCR face two major limitations. First, they are
  evaluated on relatively small-scale retrieval corpora (e.g., 100-55K cases) and
  use a narrow range of criminal query types, which cannot sufficiently reflect
  the complexity of real-world legal retrieval scenarios. Second, their reliance
  on embedding-based or lexical matching methods often results in limited
  representations and legally irrelevant matches. To address these issues, we
  present: (1) LEGAR BENCH, the first large-scale Korean LCR benchmark, covering
  411 diverse crime types in queries over 1.2M legal cases; and (2)
  LegalSearchLM, a retrieval model that performs legal element reasoning over the
  query case and directly generates content grounded in the target cases through
  constrained decoding. Experimental results show that LegalSearchLM outperforms
  baselines by 6-20% on LEGAR BENCH, achieving state-of-the-art performance. It
  also demonstrates strong generalization to out-of-domain cases, outperforming
  naive generative models trained on in-domain data by 15%.
paper_authors._id: 683d67343f97feb881211cf8
paper_authors.name: Chaeeun Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T09:56:37.502Z
paper_authors._id: 614c9487cbb5e52274a4024d
paper_authors.avatarUrl: /avatars/a923db5ea27c4184ed2ce84738860203.svg
paper_authors.fullname: Chaeeun Kim
paper_authors.type: user
paper_authors.user: Chaeeun-Kim
paper_authors._id: 683d67343f97feb881211cf9
paper_authors.name: Jinu Lee
paper_authors._id: 683d67343f97feb881211cfa
paper_authors.name: Wonseok Hwang
paper_title: LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements
    Generation
paper_summary: Legal Case Retrieval (LCR), which retrieves relevant cases from a query case,
  is a fundamental task for legal professionals in research and decision-making.
  However, existing studies on LCR face two major limitations. First, they are
  evaluated on relatively small-scale retrieval corpora (e.g., 100-55K cases) and
  use a narrow range of criminal query types, which cannot sufficiently reflect
  the complexity of real-world legal retrieval scenarios. Second, their reliance
  on embedding-based or lexical matching methods often results in limited
  representations and legally irrelevant matches. To address these issues, we
  present: (1) LEGAR BENCH, the first large-scale Korean LCR benchmark, covering
  411 diverse crime types in queries over 1.2M legal cases; and (2)
  LegalSearchLM, a retrieval model that performs legal element reasoning over the
  query case and directly generates content grounded in the target cases through
  constrained decoding. Experimental results show that LegalSearchLM outperforms
  baselines by 6-20% on LEGAR BENCH, achieving state-of-the-art performance. It
  also demonstrates strong generalization to out-of-domain cases, outperforming
  naive generative models trained on in-domain data by 15%.
paper_ai_summary: LegalSearchLM outperforms existing models in retrieving relevant legal cases by incorporating comprehensive reasoning and content generation, demonstrated on LEGAR BENCH, a large-scale Korean LCR benchmark.
paper_ai_keywords: embedding-based lexical matching LEGAR BENCH LegalSearchLM legal element reasoning constrained decoding