AIConfPaper
← Search

Shubham Kumar Nigam

5 accepted papers

2025

LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification

Shubham Kumar Nigam, Tanmay Dubey, Govind Sharma, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
NAACL 2025findings

In this paper, we address the task of semantic segmentation of legal documents through rhetorical role classification, with a focus on Indian legal judgments. We introduce **LegalSeg**, the largest annotated dataset for this task, comprising over 7,000 documents and 1.4 million sentences, labeled wi…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

NYAYAANUMANA and INLEGALLLAMA: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis

Shubham Kumar Nigam, Deepak Patnaik Balaramamahanthi, Shivam Mishra, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
COLING 2025main

The integration of artificial intelligence (AI) in legal judgment prediction (LJP) has the potential to transform the legal landscape, particularly in jurisdictions like India, where a significant backlog of cases burdens the legal system. This paper introduces NyayaAnumana, the largest and most div…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts

Shubham Kumar Nigam, Anurag Sharma, Danush Khanna, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya
ACL 2024findings

In the era of Large Language Models (LLMs), predicting judicial outcomes poses significant challenges due to the complexity of legal proceedings and the scarcity of expert-annotated datasets. Addressing this, we introduce Prediction with Explanation (PredEx), the largest expert-annotated dataset for…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2023

LLMs -- the Good, the Bad or the Indispensable?: A Use Case on Legal Statute Prediction and Legal Judgment Prediction on Indian Court Cases

Shaurya Vats, Atharva Zope, Somsubhra De, Anurag Sharma, Upal Bhattacharya, Shubham Kumar Nigam +3
EMNLP 2023short findings

The Large Language Models (LLMs) have impacted many real-life tasks. To examine the efficacy of LLMs in a high-stake domain like law, we have applied state-of-the-art LLMs for two popular tasks: Statute Prediction and Judgment Prediction, on Indian Supreme Court cases. We see that while LLMs exhibit…

Cited by 0Source ↗Scholar ↗
2021

ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation

Vijit Malik, Rishabh Sanjay, Shubham Kumar Nigam, Kripabandhu Ghosh, Shouvik Kumar Guha, Arnab Bhattacharya +1
ACL 2021long

An automated system that could assist a judge in predicting the outcome of a case would help expedite the judicial process. For such a system to be practically useful, predictions by the system should be explainable. To promote research in developing such a system, we introduce ILDC (Indian Legal Do…

Cited by 156Source ↗PDF ↗code ↗Scholar ↗