Papers by Souradip Chakraborty
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. |
Jailbreaks as Inference-Time Alignment: A Framework for Understanding Safety Failures in LLMs (2026.eacl-long)
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| Challenge: | Large language models are safety-aligned to prevent harmful response generation . prior work on jailbreak effectiveness has focused on analyzing success rate of jailbreaks . |
| Approach: | They propose to frame jailbreaks as inference-time alignment and draw suboptimal bounds . they also propose a Safety-Net to measure how vulnerable an LLM is to jailbreak attacks . |
| Outcome: | a new framework allows researchers to show how vulnerable an LLM is to jailbreaks . a Safety-Net measures how vulnerable the model is to attacks, the authors say . |
BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR (2020.coling-main)
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| Challenge: | SARS-CoV-2 pandemic highlighted importance of moving quickly with biomedical research. |
| Approach: | They propose a textual data mining tool that supports literature search to accelerate the work of researchers in the biomedical domain. |
| Outcome: | The proposed model achieves state-of-the-art results on the QA fine-tuning task on BioASQ 5b, 6b and 7b datasets. |