5 citations · 18 across the 20 of their papers we have counts for
18 papers · 1 filter
Can We Optimize the Performance-Carbon Emission Break-Even Point?: The Quest for Greener LLMs
Sourav Das, Tanmay Joshi, Kripabandhu Ghosh
The carbon footprint of any deployed Large Language Model (LLM) accumulates during inference, where repeated use of the model substantially exceeds the one-time cost of fine-tuning…
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…
Don't Judge a Book by its Cover: Testing LLMs' Robustness Under Logical Obfuscation
Abhilekh Borah, Shubhra Ghosh, Kedar Joshi +2
Tasks such as solving arithmetic equations, evaluating truth tables, and completing syllogisms are handled well by large language models (LLMs) in their standard form, but they oft…
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…
Causal Reasoning Favors Encoders: On The Limits of Decoder-Only Models
Amartya Roy, Elamparithy M, Kripabandhu Ghosh +2
In context learning (ICL) underpins recent advances in large language models (LLMs), although its role and performance in causal reasoning remains unclear. Causal reasoning demands…
SMITE: Enhancing Fairness in LLMs through Optimal In-Context Example Selection via Dynamic Validation
Garima Chhikara, Kripabandhu Ghosh, Abhijnan Chakraborty
Large Language Models (LLMs) are widely used for downstream tasks such as tabular classification, where ensuring fairness in their outputs is critical for inclusivity, equal repres…
ObfusQAte: A Proposed Framework to Evaluate LLM Robustness on Obfuscated Factual Question Answering
Shubhra Ghosh, Abhilekh Borah, Aditya Kumar Guru +1
The rapid proliferation of Large Language Models (LLMs) has significantly contributed to the development of equitable AI systems capable of factual question-answering (QA). However…
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…
QuickSilver -- Speeding up LLM Inference through Dynamic Token Halting, KV Skipping, Contextual Token Fusion, and Adaptive Matryoshka Quantization
Danush Khanna, Aditya Kumar Guru, Srivarshinee Sridhar +7
Inference accounts for the majority of latency and energy consumption in large language model (LLM) deployments, often exceeding 90% of total cost. While training-time efficiency h…
SELF-PERCEPT: Introspection Improves Large Language Models' Detection of Multi-Person Mental Manipulation in Conversations
Danush Khanna, Pratinav Seth, Sidhaarth Sredharan Murali +5
Mental manipulation is a subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential victims. However, due to manipu…
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…
Do the Right Thing, Just Debias! Multi-Category Bias Mitigation Using LLMs
Amartya Roy, Danush Khanna, Devanshu Mahapatra +3
This paper tackles the challenge of building robust and generalizable bias mitigation models for language. Recognizing the limitations of existing datasets, we introduce ANUBIS, a…
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…
LaMSUM: Amplifying Voices Against Harassment through LLM Guided Extractive Summarization of User Incident Reports
Garima Chhikara, Anurag Sharma, V. Gurucharan +2
Citizen reporting platforms help the public and authorities stay informed about sexual harassment incidents. However, the high volume of data shared on these platforms makes review…
Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification
Garima Chhikara, Anurag Sharma, Kripabandhu Ghosh +1
Employing Large Language Models (LLM) in various downstream applications such as classification is crucial, especially for smaller companies lacking the expertise and resources req…