4 citations · 6 across the 7 of their papers we have counts for
7 papers
PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation
Devanshu Verma, Vasudha Bhatnagar, Vikas Kumar +1
Legal precedent retrieval is a fundamental task in legal case preparation, planning, litigation strategy, and legal research. Current approaches for automatic precedent retrieval m…
SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models
Debarun Bhattacharjya, Balaji Ganesan, Junkyu Lee +4
When does a large language model (LLM) know what it does not know? Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM…
The Consistency Hypothesis in Uncertainty Quantification for Large Language Models
Quan Xiao, Debarun Bhattacharjya, Balaji Ganesan +5
Estimating the confidence of large language model (LLM) outputs is essential for real-world applications requiring high user trust. Black-box uncertainty quantification (UQ) method…
Human Centered AI for Indian Legal Text Analytics
Sudipto Ghosh, Devanshu Verma, Balaji Ganesan +3
Legal research is a crucial task in the practice of law. It requires intense human effort and intellectual prudence to research a legal case and prepare arguments. Recent boom in g…
Infusing Knowledge into Large Language Models with Contextual Prompts
Kinshuk Vasisht, Balaji Ganesan, Vikas Kumar +1
Knowledge infusion is a promising method for enhancing Large Language Models for domain-specific NLP tasks rather than pre-training models over large data from scratch. These augme…
Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning
Debarun Bhattacharjya, Junkyu Lee, Don Joven Agravante +2
Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks. However, they exhibit numerous limitat…
Automated Answer Validation using Text Similarity
Balaji Ganesan, Arjun Ravikumar, Lakshay Piplani +5
Automated answer validation can help improve learning outcomes by providing appropriate feedback to learners, and by making question answering systems and online learning solutions…