Papers by Varun Chandrasekaran
Bypassing LLM Watermarks with Color-Aware Substitutions (2024.acl-long)
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| Challenge: | Existing methods to detect large language models (LLMs) generated text are susceptible to a large number of false positives. |
| Approach: | They propose a watermarking approach that uses color information to determine token colors and substitute green tokens with non-green ones. |
| Outcome: | The proposed method evades detection with fewer edits and removes the watermark for arbitrarily long watermarked text. |
Designing Informative Metrics for Few-Shot Example Selection (2024.findings-acl)
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| Challenge: | Pretrained language models (PLMs) have shown remarkable few-shot learning capabilities when provided with properly formatted examples. |
| Approach: | They propose a complexity-based prompt selection approach for sequence tagging tasks that uses certain metrics to align the syntactico-semantic complexity of test sentences and examples. |
| Outcome: | The proposed approach achieves state-of-the-art performance on few-shot NER, with 5% improvement in F1 score. |
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. |
Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models (2025.acl-long)
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| Challenge: | ambiguous comparative prompting frameworks generate biased content when confronted with ambiguously prompts . ambiguity in LLMs can manifest in various forms, including reinforcing stereotypes, displaying gender bias, or demonstrating preferential treatment towards specific demographic groups. |
| Approach: | They propose a technique to localize bias to specific layers of an LLM by analyzing attention scores and then reduce bias by scaling attention in these biased layers. |
| Outcome: | The proposed technique reduces bias by scaling attention in biased layers. |