LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements
  Generation
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%.
683d67343f97feb881211cf8
Chaeeun Kim
claimed_verified
2025-06-04T09:56:37.502Z
614c9487cbb5e52274a4024d
/avatars/a923db5ea27c4184ed2ce84738860203.svg
Chaeeun Kim
user
Chaeeun-Kim
683d67343f97feb881211cf9
Jinu Lee
683d67343f97feb881211cfa
Wonseok Hwang
LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements
  Generation
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%.
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.
embedding-based lexical matching LEGAR BENCH LegalSearchLM legal element reasoning constrained decoding