LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements
  Generation
Authors: name: Chaeeun Kim, name: Jinu Lee, name: Wonseok Hwang

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%.