Rhetorical-Role-Aware Retrieval-Augmented Generation for Legal Question Answering over Indian Supreme Court Judgments

• URL: http://arxiv.org/abs/2608.06828v1
• Date: Fri, 07 Aug 2026 05:38:02 GMT
• Title: Rhetorical-Role-Aware Retrieval-Augmented Generation for Legal Question Answering over Indian Supreme Court Judgments
• Abstract summary: This research paper proposes a Retrieval Augmented Generation (RAG) framework that is specific to the legal field in order to assist interactive retrieval and reason about judgments from the Supreme Court of India.<n>The solution uses an enhanced version of RAG framework which consists of rhetorically based chunking, fusion-based retrieval, and cross encoder reranking methods to increase the relevancy of the information retrieved.
• Score: 0.4893345190925178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research paper proposes a Retrieval Augmented Generation (RAG) framework that is specific to the legal field in order to assist interactive retrieval and reason about judgments from the Supreme Court of India. The solution uses an enhanced version of RAG framework which consists of rhetorically based chunking, fusion-based retrieval, and cross encoder reranking methods to increase the relevancy of the information retrieved. In order to improve conversations, the proposed framework uses chat history along with query classification and rewriting in order to understand user intention from successive queries. Additionally, there are features that take into account structural aspects of legal documents, such as isolated names of judges that could have an impact on retrieval quality. The evaluation was done using the DeepEval framework and demonstrated strong performance on metrics including contextual recall and answer relevancy, which proves that the framework is very effective in dealing with legal question-answering tasks that require a lot of context. The results emphasize the importance of domain specific enhancements in developing legal AI systems that are both reliable and explainable.

Related papers

• AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System [11.383388187292674]
  This study introduces an advanced Artificial Intelligence for Indian Legal Question Answering (AILQA) system tailored to the Indian legal context.<n>AILQA leverages a variety of embedding and generative models, including recent Large Language Models (LLMs)<n>We conducted rigorous evaluations using both lexical and semantic metrics, enriched by expert legal feedback, to ensure relevance and accuracy.
  arXiv  Detail & Related papers  (2026-07-21T08:01:17Z)
• R$^2$-Searcher: Calibrating Retrieval and Reasoning Boundaries for Agentic Search [59.034878430183305]
  R$2$-Searcher explicitly explores and calibrates the retrieval and reasoning boundaries.<n>We show that R$2$-Searcher significantly outperforms state-of-the-art agentic search methods in answer accuracy and retrieval-reasoning quality.
  arXiv  Detail & Related papers  (2026-06-26T19:44:34Z)
• Fine-grained Claim-level RAG Benchmark for Law [2.807618472608]
  In high-stake domains such as law, retrieval-augmented generation (RAG) is commonly used to mitigate hallucinations in generated responses.<n>Existing evaluation frameworks for legal RAG systems lack the granularity required to provide detailed analysis of retrieval and generation performance.<n>We introduce ClaimRAG-LAW, a comprehensive dataset for legal RAG that supports French and English, targets both experts and non-experts, and includes diverse question types reflecting realistic scenarios.
  arXiv  Detail & Related papers  (2026-05-20T11:56:27Z)
• Verbal-R3: Verbal Reranker as the Missing Bridge between Retrieval and Reasoning [48.15732775780642]
  This paper proposes to bridge retrieval results and the Large Language Model's reasoning ability through Verbal s.<n>We introduce Verbal-R3, a novel agentic RAG framework that consists of a Generator and a Verbal Reranker.<n> Verbal-R3 achieves state-of-the-art performance on complex Question Answering benchmarks, validating the effectiveness of the proposed framework.
  arXiv  Detail & Related papers  (2026-05-02T11:43:23Z)
• RAC: Retrieval-Augmented Clarification for Faithful Conversational Search [7.0486278653981245]
  We introduce RAC (Retrieval-Augmented Clarification), a framework for generating corpus-faithful clarification questions.<n>After comparing several indexing strategies for retrieval, we fine-tune a large language model to make optimal use of research context.<n>We then apply contrastive preference optimization to favor questions supported by retrieved passages over ungrounded alternatives.
  arXiv  Detail & Related papers  (2026-01-16T19:16:38Z)
• Domain-Partitioned Hybrid RAG for Legal Reasoning: Toward Modular and Explainable Legal AI for India [5.022547031373416]
  Legal research in India involves navigating long and heterogeneous documents spanning statutes, constitutional provisions, penal codes, and judicial precedents.<n>Recent retrieval augmented generation (RAG) approaches improve grounding but struggle with multi-hop reasoning.<n>We propose a domain partitioned hybrid RAG and Knowledge Graph architecture designed specifically for Indian legal research.
  arXiv  Detail & Related papers  (2025-12-23T20:22:39Z)
• Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce [61.03081096959132]
  We propose a context-aware reasoning-enhanced generative search framework for better textbfunderstanding the complicated context.<n>Our approach achieves superior performance compared with strong baselines, validating its effectiveness for search-based recommendation.
  arXiv  Detail & Related papers  (2025-10-19T16:46:11Z)
• Augmented Question-guided Retrieval (AQgR) of Indian Case Law with LLM, RAG, and Structured Summaries [0.0]
  This paper proposes the use of Large Language Models (LLMs) to facilitate the retrieval of relevant cases.<n>Our approach combines Retrieval Augmented Generation (RAG) with structured summaries optimized for Indian case law.<n>The system generates targeted legal questions based on factual scenarios to identify relevant case law more effectively.
  arXiv  Detail & Related papers  (2025-07-23T05:24:44Z)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively.<n>We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• 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)
• Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy [66.95501113584541]
  We propose an Iterative utiliTy judgm fraEntMework (ITEM) to promote each step in Retrieval-Augmented Generation (RAG)<n>RAG's three core components -- relevance ranking derived from retrieval models, utility judgments, and answer generation -- align with Schutz's philosophical system of relevances.<n> Experimental results demonstrate significant improvements of ITEM in utility judgments, ranking, and answer generation upon representative baselines.
  arXiv  Detail & Related papers  (2024-06-17T07:52:42Z)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)

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.