5 papers
Rethinking LLM Verification: Evidence Structure, Uncertainty, and Selective Refinement
Uma Ranjan, Kunal Tilaganji, Aditya Koul +9
Large language models (LLMs) often rely on shortcuts rather than systematic reasoning, raising safety concerns in medical applications. Allowing models to abstain when uncertain im…
VERDICT: Training-Free Step-Wise Verification of Multimodal Reasoning via Disagreement-Aware Consensus
Rohit Sinha, Kunal Tilaganji, Tanuja Ganu +3
Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers. Current verification approaches have notable limitations.…
A Nash Equilibrium Framework For Training-Free Multimodal Step Verification
Rohit Sinha, Kunal Tilaganji, Tanuja Ganu +3
Multimodal large language models often generate reasoning chains containing subtle errors that lead to incorrect answers. Current verification approaches have notable limitations.…
FrugalRAG: Less is More in RL Finetuning for Multi-Hop Question Answering
Abhinav Java, Srivathsan Koundinyan, Nagarajan Natarajan +1
Reinforcement learning (RL) based on the final answer's reward has driven recent progress in small language models (SLMs) on reasoning-heavy tasks such as math and code. However, a…
ASTRA: Accurate and Scalable ANNS-based Training of Extreme Classifiers
Sonu Mehta, Jayashree Mohan, Nagarajan Natarajan +2
`Extreme Classification'' (or XC) is the task of annotating data points (queries) with relevant labels (documents), from an extremely large set of possible labels, arising in s…