3 citations · 6 across the 13 of their papers we have counts for
16 papers · 1 filter
AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System
Shubham Kumar Nigam, Shubham Kumar Mishra, Noel Shallum +2
This comprehensive study introduces an advanced Artificial Intelligence for Indian Legal Question Answering (AILQA) system tailored to the Indian legal context. AILQA leverages a v…
NyayaMind- A Framework for Transparent Legal Reasoning and Judgment Prediction in the Indian Legal System
Parjanya Aditya Shukla, Shubham Kumar Nigam, Debtanu Datta +5
Court Judgment Prediction and Explanation (CJPE) aims to predict a judicial decision and provide a legally grounded explanation for a given case based on the facts, legal issues, a…
ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India
Shubham Kumar Nigam, Tanuj Tyagi, Siddharth Shukla +6
This paper presents an early exploration of reinforcement learning methodologies for legal AI in the Indian context. We introduce Reinforcement Learning-based Legal Reasoning (ReGa…
IBPS: Indian Bail Prediction System
Puspesh Kumar Srivastava, Uddeshya Raj, Praveen Patel +3
Bail decisions are among the most frequently adjudicated matters in Indian courts, yet they remain plagued by subjectivity, delays, and inconsistencies. With over 75% of India's pr…
NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System
Shubham Kumar Nigam, Balaramamahanthi Deepak Patnaik, Shivam Mishra +4
Legal Judgment Prediction (LJP) has emerged as a key area in AI for law, aiming to automate judicial outcome forecasting and enhance interpretability in legal reasoning. While prev…
Segment First, Retrieve Better: Realistic Legal Search via Rhetorical Role-Based Queries
Shubham Kumar Nigam, Tanmay Dubey, Noel Shallum +1
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 growi…
TathyaNyaya and FactLegalLlama: Advancing Factual Judgment Prediction and Explanation in the Indian Legal Context
Shubham Kumar Nigam, Balaramamahanthi Deepak Patnaik, Shivam Mishra +3
In the landscape of Fact-based Judgment Prediction and Explanation (FJPE), reliance on factual data is essential for developing robust and realistic AI-driven decision-making tools…
Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej
Shubham Kumar Nigam, Balaramamahanthi Deepak Patnaik, Noel Shallum +2
Automating legal document drafting can improve efficiency and reduce the burden of manual legal work. Yet, the structured generation of private legal documents remains underexplore…
LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification
Shubham Kumar Nigam, Tanmay Dubey, Govind Sharma +3
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…
NyayaAnumana & INLegalLlama: The Largest Indian Legal Judgment Prediction Dataset and Specialized Language Model for Enhanced Decision Analysis
Shubham Kumar Nigam, Balaramamahanthi Deepak Patnaik, Shivam Mishra +3
The integration of artificial intelligence (AI) in legal judgment prediction (LJP) has the potential to transform the legal landscape, particularly in jurisdictions like India, whe…
Rethinking Legal Judgement Prediction in a Realistic Scenario in the Era of Large Language Models
Shubham Kumar Nigam, Aniket Deroy, Subhankar Maity +1
This study investigates judgment prediction in a realistic scenario within the context of Indian judgments, utilizing a range of transformer-based models, including InLegalBERT, BE…
Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts
Shubham Kumar Nigam, Anurag Sharma, Danush Khanna +3
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-annotate…
Leveraging LLMs for Bangla Grammar Error Correction:Error Categorization, Synthetic Data, and Model Evaluation
Pramit Bhattacharyya, Arnab Bhattacharya
Large Language Models (LLMs) perform exceedingly well in Natural Language Understanding (NLU) tasks for many languages including English. However, despite being the fifth most-spok…
PARAMANU-GANITA: Can Small Math Language Models Rival with Large Language Models on Mathematical Reasoning?
Mitodru Niyogi, Arnab Bhattacharya
In this paper, we study whether domain specific pretraining of small generative language models (SLM) from scratch with domain specialized tokenizer and Chain-of-Thought (CoT) inst…
Ayn: A Tiny yet Competitive Indian Legal Language Model Pretrained from Scratch
Mitodru Niyogi, Eric Gaussier, Arnab Bhattacharya
Decoder-only Large Language Models (LLMs) are currently the model of choice for many Natural Language Processing (NLP) applications. Through instruction fine-tuning and prompting a…
Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages
Mitodru Niyogi, Eric Gaussier, Arnab Bhattacharya
Multilingual large language models (LLMs) are expensive to pretrain and often suffer from imbalances across languages and datasets, English-centric bias, tokenizer oversegmentation…