3 papers
cs.CL2025
Learning Together to Perform Better: Teaching Small-Scale LLMs to Collaborate via Preferential Rationale Tuning
Sohan Patnaik, Milan Aggarwal, Sumit Bhatia +1
LLMssuch as GPT-4 have shown a remarkable ability to solve complex questions by generating step-by-step rationales. Prior works have utilized this capability to improve smaller and…
cs.CL2024
CABINET: Content Relevance based Noise Reduction for Table Question Answering
Sohan Patnaik, Heril Changwal, Milan Aggarwal +3
Table understanding capability of Large Language Models (LLMs) has been extensively studied through the task of question-answering (QA) over tables. Typically, only a small part of…
cs.CL2022
LM-CORE: Language Models with Contextually Relevant External Knowledge
Jivat Neet Kaur, Sumit Bhatia, Milan Aggarwal +2
Large transformer-based pre-trained language models have achieved impressive performance on a variety of knowledge-intensive tasks and can capture factual knowledge in their parame…