1 citations · 1 across the 4 of their papers we have counts for
6 papers
One Size Does Not Fit All! Dynamic Retriever and Generator Selection for RAG
Neeraj Anand, Payel Santra, Partha Basuchowdhuri +2
Retrieval-Augmented Generation (RAG) systems typically employ fixed retriever and generator configurations across queries, despite substantial differences in query complexity and i…
Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?
H S V N S Kowndinya Renduchintala, Sumit Bhatia
Large Language Models (LLMs) exhibit a puzzling disparity in their formal linguistic competence: while they learn some linguistic phenomena with near-perfect mastery, they often pe…
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…
It Helps to Take a Second Opinion: Teaching Smaller LLMs to Deliberate Mutually via Selective Rationale Optimisation
Sohan Patnaik, Milan Aggarwal, Sumit Bhatia +1
Very large language models (LLMs) such as GPT-4 have shown the ability to handle complex tasks by generating and self-refining step-by-step rationales. Smaller language models (SLM…
Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models
Shaz Furniturewala, Surgan Jandial, Abhinav Java +4
Existing debiasing techniques are typically training-based or require access to the model's internals and output distributions, so they are inaccessible to end-users looking to ada…
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…