3 citations · 6 across the 13 of their papers we have counts for
7 papers · 2 filters
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…