Papers by Anirudh Satheesh
Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems (2025.findings-emnlp)
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Aakriti Agrawal, Rohith Aralikatti, Anirudh Satheesh, Souradip Chakraborty, Amrit Singh Bedi, Furong Huang
| Challenge: | Existing approaches to selecting reliable responses from multiple LLMs often depend on external verifiers, human evaluators, or self-consistency techniques. |
| Approach: | They propose a calibrated log-likelihood-based selection framework to improve multi-LLM performance. |
| Outcome: | The proposed method outperforms majority voting and exceeds self-consistency performance when using a large number of model calls. |
EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles (2026.findings-acl)
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Aakriti Agrawal, Mucong Ding, Chenghao Deng, Zora Che, Arjun Rajaram, Anirudh Satheesh, Bang An, C. Bayan Bruss, John Langford, Furong Huang
| Challenge: | Large Language Models (LLMs) are rapidly approaching and potentially exceeding human-level performance . a novel method aims to improve weak experts' generalization abilities by training them on limited human- level data . |
| Approach: | They propose a method that iteratively combines multiple weak experts to improve their generalization performance by training on limited human-level data. |
| Outcome: | The proposed method improves weak experts' generalization abilities by iterating on weak models and stronger student models. |