Segment First, Retrieve Better: Realistic Legal Search via Rhetorical Role-Based Queries

• URL: http://arxiv.org/abs/2508.00679v1
• Date: Fri, 01 Aug 2025 14:49:33 GMT
• Title: Segment First, Retrieve Better: Realistic Legal Search via Rhetorical Role-Based Queries
• Authors: Shubham Kumar Nigam, Tanmay Dubey, Noel Shallum, Arnab Bhattacharya,
• Abstract summary: TraceRetriever mirrors real-world legal search by operating with limited case information.<n>Our pipeline integrates BM25, Vector Database, and Cross-Encoder models, combining initial results through Reciprocal Rank Fusion.<n> Rhetorical annotations are generated using a Hierarchical BiLSTM CRF classifier trained on Indian judgments.
• Score: 3.552993426200889
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Legal precedent retrieval is a cornerstone of the common law system, governed by the principle of stare decisis, which demands consistency in judicial decisions. However, the growing complexity and volume of legal documents challenge traditional retrieval methods. TraceRetriever mirrors real-world legal search by operating with limited case information, extracting only rhetorically significant segments instead of requiring complete documents. Our pipeline integrates BM25, Vector Database, and Cross-Encoder models, combining initial results through Reciprocal Rank Fusion before final re-ranking. Rhetorical annotations are generated using a Hierarchical BiLSTM CRF classifier trained on Indian judgments. Evaluated on IL-PCR and COLIEE 2025 datasets, TraceRetriever addresses growing document volume challenges while aligning with practical search constraints, reliable and scalable foundation for precedent retrieval enhancing legal research when only partial case knowledge is available.

Related papers

• LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation [5.243460995467895]
  We present LEGAR BENCH, the first large-scale Korean Legal Case Retrieval benchmark, covering 411 diverse crime types in queries over 1.2M legal cases.<n>We also present LegalSearchLM, a retrieval model that performs legal element reasoning over the query case and directly generates content grounded in the target cases.
  arXiv  Detail & Related papers  (2025-05-28T09:02:41Z)
• A Reproducibility Study of Graph-Based Legal Case Retrieval [1.6819960041696331]
  CaseLink is a graph-based method for legal case retrieval.<n>CaseLink captures higher-order relationships of cases going beyond the stand-alone level of documents.<n>Challenges in reproducing novel results have recently been highlighted.
  arXiv  Detail & Related papers  (2025-04-11T10:04:12Z)
• JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking [81.88787401178378]
  We introduce JudgeRank, a novel agentic reranker that emulates human cognitive processes when assessing document relevance. We evaluate JudgeRank on the reasoning-intensive BRIGHT benchmark, demonstrating substantial performance improvements over first-stage retrieval methods. In addition, JudgeRank performs on par with fine-tuned state-of-the-art rerankers on the popular BEIR benchmark, validating its zero-shot generalization capability.
  arXiv  Detail & Related papers  (2024-10-31T18:43:12Z)
• Exploiting LLMs' Reasoning Capability to Infer Implicit Concepts in Legal Information Retrieval [6.952344923975001]
  This work focuses on utilizing the logical reasoning capabilities of large language models (LLMs) to identify relevant legal terms. The proposed retrieval system integrates additional information from the term--based expansion and query reformulation to improve the retrieval accuracy. Experiments on COLIEE 2022 and COLIEE 2023 datasets show that extra knowledge from LLMs helps to improve the retrieval result of both lexical and semantic ranking models.
  arXiv  Detail & Related papers  (2024-10-16T01:34:14Z)
• Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs [67.54302101989542]
  Legal case retrieval aims to provide similar cases as references for a given fact description. Existing works mainly focus on case-to-case retrieval using lengthy queries. Data scale is insufficient to satisfy the training requirements of existing data-hungry neural models.
  arXiv  Detail & Related papers  (2024-10-09T06:26:39Z)
• STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals [14.002280587675175]
  Statute retrieval aims to find relevant statutory articles for specific queries. Existing statute retrieval benchmarks focus on formal and professional queries from sources like bar exams and legal case documents. To address this gap, we introduce the STAtute Retrieval dataset (STARD) Unlike existing statute retrieval datasets, STARD captures the complexity and diversity of real queries from the general public.
  arXiv  Detail & Related papers  (2024-06-21T17:10:09Z)
• MUSER: A Multi-View Similar Case Retrieval Dataset [65.36779942237357]
  Similar case retrieval (SCR) is a representative legal AI application that plays a pivotal role in promoting judicial fairness. Existing SCR datasets only focus on the fact description section when judging the similarity between cases. We present M, a similar case retrieval dataset based on multi-view similarity measurement and comprehensive legal element with sentence-level legal element annotations.
  arXiv  Detail & Related papers  (2023-10-24T08:17:11Z)
• U-CREAT: Unsupervised Case Retrieval using Events extrAcTion [2.2385755093672044]
  We propose a new benchmark (in English) for the Prior Case Retrieval task: IL-PCR (Indian Legal Prior Case Retrieval) corpus. We explore the role of events in legal case retrieval and propose an unsupervised retrieval method-based pipeline U-CREAT. We find that the proposed unsupervised retrieval method significantly increases performance compared to BM25 and makes retrieval faster by a considerable margin.
  arXiv  Detail & Related papers  (2023-07-11T13:51:12Z)
• How Does Generative Retrieval Scale to Millions of Passages? [68.98628807288972]
  We conduct the first empirical study of generative retrieval techniques across various corpus scales. We scale generative retrieval to millions of passages with a corpus of 8.8M passages and evaluating model sizes up to 11B parameters. While generative retrieval is competitive with state-of-the-art dual encoders on small corpora, scaling to millions of passages remains an important and unsolved challenge.
  arXiv  Detail & Related papers  (2023-05-19T17:33:38Z)
• UnifieR: A Unified Retriever for Large-Scale Retrieval [84.61239936314597]
  Large-scale retrieval is to recall relevant documents from a huge collection given a query. Recent retrieval methods based on pre-trained language models (PLM) can be coarsely categorized into either dense-vector or lexicon-based paradigms. We propose a new learning framework, UnifieR which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability.
  arXiv  Detail & Related papers  (2022-05-23T11:01:59Z)
• GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
  We propose GERE, the first system that retrieves evidences in a generative fashion. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-04-12T03:49:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.