Papers by Balaraman Ravindran
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. |
Towards Transparent and Explainable Attention Models (2020.acl-main)
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Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan, Mitesh M. Khapra, Balaji Vasan Srinivasan, Balaraman Ravindran
| Challenge: | Recent studies on interpretability of attention distributions have led to notions of faithful and plausible explanations for a model’s predictions. |
| Approach: | They propose to modify LSTM cells to ensure that the hidden representations learned at different time steps are diverse. |
| Outcome: | The proposed model can provide a faithful explanation if a higher attention weight implies a greater impact on the model’s prediction. |
Let’s Ask Again: Refine Network for Automatic Question Generation (D19-1)
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Preksha Nema, Akash Kumar Mohankumar, Mitesh M. Khapra, Balaji Vasan Srinivasan, Balaraman Ravindran
| Challenge: | Existing AQG models produce incomplete questions which look like incomplete drafts with scope for refinement. |
| Approach: | They propose a method which mimics the human process of generating questions by first creating an initial draft and then refining it. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three datasets and improves on fluency and answerability metrics. |