Structured Definitions and Segmentations for Legal Reasoning in LLMs: A Study on Indian Legal Data

• URL: http://arxiv.org/abs/2511.20669v1
• Date: Fri, 14 Nov 2025 13:24:00 GMT
• Title: Structured Definitions and Segmentations for Legal Reasoning in LLMs: A Study on Indian Legal Data
• Authors: Mann Khatri, Mirza Yusuf, Rajiv Ratn Shah, Ponnurangam Kumaraguru,
• Abstract summary: Large Language Models (LLMs), trained on extensive datasets from the web, exhibit remarkable general reasoning skills.<n>However, they often struggle in specialized areas like law, mainly because they lack domain-specific pretraining.<n>Previous studies have examined in-context approaches to address the knowledge gap, boosting model performance in new domains without full domain alignment.
• Score: 27.162165587035176
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs), trained on extensive datasets from the web, exhibit remarkable general reasoning skills. Despite this, they often struggle in specialized areas like law, mainly because they lack domain-specific pretraining. The legal field presents unique challenges, as legal documents are generally long and intricate, making it hard for models to process the full text efficiently. Previous studies have examined in-context approaches to address the knowledge gap, boosting model performance in new domains without full domain alignment. In our paper, we analyze model behavior on legal tasks by conducting experiments in three areas: (i) reorganizing documents based on rhetorical roles to assess how structured information affects long context processing and model decisions, (ii) defining rhetorical roles to familiarize the model with legal terminology, and (iii) emulating the step-by-step reasoning of courts regarding rhetorical roles to enhance model reasoning. These experiments are conducted in a zero-shot setting across three Indian legal judgment prediction datasets. Our results reveal that organizing data or explaining key legal terms significantly boosts model performance, with a minimum increase of ~1.5% and a maximum improvement of 4.36% in F1 score compared to the baseline.

Related papers

• LegalOne: A Family of Foundation Models for Reliable Legal Reasoning [54.57434222018289]
  We present LegalOne, a family of foundational models specifically tailored for the Chinese legal domain.<n>LegalOne is developed through a comprehensive three-phase pipeline designed to master legal reasoning.<n>We publicly release the LegalOne weights and the LegalKit evaluation framework to advance the field of Legal AI.
  arXiv  Detail & Related papers  (2026-01-31T10:18:32Z)
• PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice [67.71760070255425]
  We introduce PLawBench, a practical benchmark for evaluating large language models (LLMs) in legal practice scenarios.<n>PLawBench comprises 850 questions across 13 practical legal scenarios, with each question accompanied by expert-designed evaluation rubrics.<n>Using an LLM-based evaluator aligned with human expert judgments, we evaluate 10 state-of-the-art LLMs.
  arXiv  Detail & Related papers  (2026-01-23T11:36:10Z)
• ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation [56.79698529022327]
  Legal claims refer to the plaintiff's demands in a case and are essential to guiding judicial reasoning and case resolution.<n>This paper explores the problem of legal claim generation based on the given case's facts.<n>We construct ClaimGen-CN, the first dataset for Chinese legal claim generation task.
  arXiv  Detail & Related papers  (2025-08-24T07:19:25Z)
• LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification [6.549338652948716]
  We introduce LegalSeg, the largest annotated dataset for this task, comprising over 7,000 documents and 1.4 million sentences, labeled with 7 rhetorical roles.<n>Our results demonstrate that models incorporating broader context, structural relationships, and sequential sentence information outperform those relying solely on sentence-level features.
  arXiv  Detail & Related papers  (2025-02-09T10:07:05Z)
• InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
  InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws. We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries. InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
  arXiv  Detail & Related papers  (2024-06-21T06:19:03Z)
• Topic Classification of Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment [0.0]
  This paper develops and applies a novel taxonomy for topic classification of summary judgment cases in the United Kingdom.<n>We use the Large Language Model Claude 3 Opus to explore functional topics and trends.<n>We find that Claude 3 Opus correctly classified the topic with an accuracy of 87.13% and an F1 score of 0.87.
  arXiv  Detail & Related papers  (2024-05-21T16:30:25Z)
• Empowering Prior to Court Legal Analysis: A Transparent and Accessible Dataset for Defensive Statement Classification and Interpretation [5.646219481667151]
  This paper introduces a novel dataset tailored for classification of statements made during police interviews, prior to court proceedings. We introduce a fine-tuned DistilBERT model that achieves state-of-the-art performance in distinguishing truthful from deceptive statements. We also present an XAI interface that empowers both legal professionals and non-specialists to interact with and benefit from our system.
  arXiv  Detail & Related papers  (2024-05-17T11:22:27Z)
• DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
  We introduce DELTA, a discriminative model designed for legal case retrieval. We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Our approach can outperform existing state-of-the-art methods in legal case retrieval.
  arXiv  Detail & Related papers  (2024-03-27T10:40:14Z)
• Modeling Legal Reasoning: LM Annotation at the Edge of Human Agreement [3.537369004801589]
  We study the classification of legal reasoning according to jurisprudential philosophy. We use a novel dataset of historical United States Supreme Court opinions annotated by a team of domain experts. We find that generative models perform poorly when given instructions equal to the instructions presented to human annotators.
  arXiv  Detail & Related papers  (2023-10-27T19:27:59Z)
• Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration [52.57055162778548]
  Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
  arXiv  Detail & Related papers  (2023-10-13T16:47:20Z)
• Enhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions [0.16385815610837165]
  The size of legal opinions continues to grow, making it increasingly challenging to develop a model that can accurately predict the rhetorical roles of legal opinions. We propose a novel model architecture for automatically predicting rhetorical roles using pre-trained language models (PLMs) enhanced with knowledge of sentence position information. Based on an annotated corpus from the LegalEval@SemEval2023 competition, we demonstrate that our approach requires fewer parameters, resulting in lower computational costs.
  arXiv  Detail & Related papers  (2023-10-08T20:33:55Z)
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval [75.05173891207214]
  Legal case retrieval plays a core role in the intelligent legal system. Most existing language models have difficulty understanding the long-distance dependencies between different structures. We propose a new Structure-Aware pre-traIned language model for LEgal case Retrieval.
  arXiv  Detail & Related papers  (2023-04-22T10:47:01Z)
• When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset [2.0924876102146714]
  We present a new dataset comprised of over 53,000+ multiple choice questions to identify the relevant holding of a cited case. We show that domain pretraining may be warranted when the task exhibits sufficient similarity to the pretraining corpus. Our findings inform when researchers should engage resource-intensive pretraining and show that Transformer-based architectures, too, learn embeddings suggestive of distinct legal language.
  arXiv  Detail & Related papers  (2021-04-18T00:57:16Z)

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.