Legal research in India involves navigating long and heterogeneous documents spanning statutes, constitutional provisions, penal codes, and judicial precedents, where purely keyword-based or embedding-only retrieval systems often fail to support structured legal reasoning. Recent retrieval augmented generation (RAG) approaches improve grounding but struggle with multi-hop reasoning, citation chaining, and cross-domain dependencies inherent to legal texts.
We propose a domain partitioned hybrid RAG and Knowledge Graph architecture designed specifically for Indian legal research. The system integrates three specialized RAG pipelines covering Supreme Court case law, statutory and constitutional texts, and the Indian Penal Code, each optimized for domain specific retrieval. To enable relational reasoning beyond semantic similarity, we construct a Neo4j based Legal Knowledge Graph capturing structured relationships among cases, statutes, IPC sections, judges, and citations. An LLM driven agentic orchestrator dynamically routes queries across retrieval modules and the knowledge graph, fusing evidence into grounded and citation aware responses.
We evaluate the system using a 40 question synthetic legal question answer benchmark curated from authoritative Indian legal sources and assessed via an LLM as a Judge framework. Results show that the hybrid architecture achieves a 70 percent pass rate, substantially outperforming a RAG only baseline at 37.5 percent, with marked improvements in completeness and legal reasoning quality. These findings demonstrate that combining domain partitioned retrieval with structured relational knowledge provides a scalable and interpretable foundation for advanced legal AI systems in the Indian judicial context.
AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
Indian Institute of Technology（印度理工学院）；Symbiosis Law School（共生法学院）；Indian Institute of Science Education and Research（印度科学教育与研究学院）；University of Birmingham（伯明翰大学）
EkStep Foundation（EkStep基金会）；Thoughtworks Technologies India Pvt Ltd.（Thoughtworks技术印度私人有限公司）；Agami
Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.
NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System
Indian Institute of Technology Kanpur（印度理工学院坎浦尔分校）；SRM Institute of Science and Technology（SRM科学与技术学院）；Symbiosis Law School Pune（苏比斯法学院浦那分校）；IISER Kolkata（印度科学研究所库勒卡布尔分校）；University of Birmingham, Dubai, United Arab Emirates（布里斯托尔大学迪拜分校）
Dhirubhai Ambani Institute of Information and Communication Technology（迪鲁巴伊·安巴尼信息与通信技术学院）；Malaviya National Institute of Technology（马拉维亚国家理工学院）；Paramonic AI
Unlike the courts in western countries, public records of Indian judiciary are completely unstructured and noisy. No large scale publicly available annotated datasets of Indian legal documents exist till date. This limits the scope for legal analytics research. In this work, we propose a new dataset consisting of over 10,000 judgements delivered by the supreme court of India and their corresponding hand written summaries. The proposed dataset is pre-processed by normalising common legal abbreviations, handling spelling variations in named entities, handling bad punctuations and accurate senten…
Government College Of Engineering And Textile Technology（政府工程与纺织技术学院）；Indian Institute of Technology Patna（印度理工学院巴特那分校）
In this article, we present a description of our systems as a part of our participation in the shared task namely Artificial Intelligence for Legal Assistance (AILA 2019). This is an integral event of Forum for Information Retrieval Evaluation-2019. The outcomes of this track would be helpful for the automation of the working process of the Indian Judiciary System. The manual working procedures and documentation at any level (from lower to higher court) of the judiciary system are very complex in nature. The systems produced as a part of this track would assist the law practitioners. It would …
Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej
Indian Institute of Technology Kanpur, India（印度理工学院坎浦分校）；University of Birmingham Dubai, UAE（迪拜大学）；Symbiosis Law School Pune, India（萨比欧法学院皮尔分校）；IISER Kolkata, India（印度科学学院科钦分校）
Annotating Topical Legal Insights from Case Proceedings
Indian Institute of Science Education and Research Kolkata（印度科学教育与研究学院加尔各答分校）；University of Glasgow（格拉斯哥大学）；West Bengal National University of Juridical Sciences（西孟加拉国家司法科学大学）
Automatically understanding the rhetorical roles of sentences in a legal case judgement is an important problem to solve, since it can help in several downstream tasks like summarization of legal judgments, legal search, and so on. The task is challenging since legal case documents are usually not well-structured, and these rhetorical roles may be subjective (as evident from variation of opinions between legal experts). In this paper, we address this task for judgments from the Supreme Court of India. We label sentences in 50 documents using multiple human annotators, and perform an extensive …
IIT Kharagpur（印度理工学院卡哈拉格普尔分校）；L3S Research Center（L3S研究中心）
Automatic summarization of legal case documents is an important and practical challenge. Apart from many domain-independent text summarization algorithms that can be used for this purpose, several algorithms have been developed specifically for summarizing legal case documents. However, most of the existing algorithms do not systematically incorporate domain knowledge that specifies what information should ideally be present in a legal case document summary. To address this gap, we propose an unsupervised summarization algorithm DELSumm which is designed to systematically incorporate guideline…
