4 citations · 17 across the 13 of their papers we have counts for
7 papers · 1 filter
KItCAT: Knowledge Injection via Input Corruption for Auto-regressive Training
Meghanadh Pulivarthi, Kushagra Bhushan, Vineet Kumar +5
LLMs acquire vast amounts of knowledge during pre-training, but often lack the specialized knowledge needed to answer questions from niche sources such as manuals or technical docu…
Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG
Kushagra Bhushan, Yatin Nandwani, Dinesh Khandelwal +4
Retrieval-Augmented Generation (RAG) has emerged as a prominent method for incorporating domain knowledge into Large Language Models (LLMs). While RAG enhances response relevance b…
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models
Sonam Gupta, Yatin Nandwani, Asaf Yehudai +3
Fine-tuning Large Language Models (LLMs) on specific datasets is a common practice to improve performance on target tasks. However, this performance gain often leads to overfitting…
Selective Self-Rehearsal: A Fine-Tuning Approach to Improve Generalization in Large Language Models
Sonam Gupta, Yatin Nandwani, Asaf Yehudai +4
Fine-tuning Large Language Models (LLMs) on specific datasets is a common practice to improve performance on target tasks. However, this performance gain often leads to overfitting…
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
Saeel Sandeep Nachane, Ojas Gramopadhye, Prateek Chanda +5
In this paper, we propose a modified version of the MedQA-USMLE dataset, named MEDQA-OPEN, which contains open-ended medical questions without options to mimic clinical scenarios,…
Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs
Yatin Nandwani, Vineet Kumar, Dinesh Raghu +2
A major concern in using deep learning based generative models for document-grounded dialogs is the potential generation of responses that are not \textit{faithful} to the underlyi…
CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion
Keshav Kolluru, Mayank Singh Chauhan, Yatin Nandwani +2
Pre-trained language models (LMs) like BERT have shown to store factual knowledge about the world. This knowledge can be used to augment the information present in Knowledge Bases,…