Papers by Somesh Jha
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown impressive capabilities in many tasks, including natural language understanding and generation. |
| Approach: | They propose a framework for adaptation with self-evaluation to improve selective prediction performance of large language models. |
| Outcome: | The proposed framework outperforms state-of-the-art selective prediction methods on QA datasets and improves the AUACC from 91.23% to 92.63% and AUROC from 74.61% to 80.25%. |
PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails (2024.acl-long)
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Neal Mangaokar, Ashish Hooda, Jihye Choi, Shreyas Chandrashekaran, Kassem Fawaz, Somesh Jha, Atul Prakash
| Challenge: | Recent work has shown that large language models are susceptible to automated jailbreak attacks that induce them to generate harmful content. |
| Approach: | They propose a two-step attack strategy that leverages a universal adversarial prefix for the Guard Model and propagates this prefix to the response. |
| Outcome: | The proposed attack strategy is successful against several open-source and closed-source implementations of Guard Models. |
SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verification (2026.acl-long)
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Tianyang Zhou, Ziyi Zhang, Haowen Lin, Somesh Jha, Mihai Christodorescu, Kirill Levchenko, Varun Chandrasekaran
| Challenge: | Large language models (LLMs) have shown promise in producing idiomatic translations, but offer no correctness guarantees. |
| Approach: | They propose a C-to-Rust translation tool that uses an initial "unidiomatic" translation followed by an "idiomatic refinement" they evaluate SACTOR on 200 programs from two datasets and two more complex scenarios . |
| Outcome: | The proposed tool delivers high end-to-end correctness and produces safe, idiomatic Rust with up to 7 fewer Clippy warnings. |