Papers by Atharvan Dogra
Language Models can Subtly Deceive Without Lying: A Case Study on Strategic Phrasing in Legislation (2025.acl-long)
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Atharvan Dogra, Krishna Pillutla, Ameet Deshpande, Ananya B. Sai, John J Nay, Tanmay Rajpurohit, Ashwin Kalyan, Balaraman Ravindran
| Challenge: | blatant lying or unintentional hallucination are common in large language models. |
| Approach: | They build a testbed mimicking a legislative environment where a corporate lobbyist module is proposing amendments to bills that benefit a specific company while evading identification by strong LLM detectors. |
| Outcome: | The proposed model can be used to detect deception in legislative environments and to optimize its phrasing to avoid detection by strong detectors. |
Engagement Undermines Safety: How Stereotypes and Toxicity Shape Humor in Language Models (2026.eacl-long)
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| Challenge: | Large language models are increasingly used for creative writing and engagement content, raising safety concerns about their outputs. |
| Approach: | They evaluate how funniness optimization in large language models couples with harmful content by jointly measuring humor, stereotypicality, and toxicity. |
| Outcome: | The proposed model couples humor, stereotypicality, and toxicity with harmful outputs . the results suggest a bias amplification loop between generators and evaluators . |