Papers by Aswin Rrv
Chaos with Keywords: Exposing Large Language Models Sycophancy to Misleading Keywords and Evaluating Defense Strategies (2024.findings-acl)
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| Challenge: | sycophancy is a type of hallucination in Large Language Models, which can lead to false information being presented. |
| Approach: | They explore the sycophantic tendencies of Large Language Models where models provide accurate answers even if they are not entirely correct. |
| Outcome: | The proposed models generate factually correct statements even when they are not completely correct. |
ThinkTuning: Instilling Cognitive Reflections without Distillation (2025.emnlp-main)
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| Challenge: | Recent advances in test-time scaling have led to the emergence of thinking LLMs that exhibit self-reflective behaviors and multi-step reasoning. |
| Approach: | They propose a GRPO-based interactive training approach that augments the rollouts of a student model with the guidance of . a teacher poses a problem, lets the student try an answer, then gives corrective feedback–enough to point the mind in the right direction and then show the correct solution. |
| Outcome: | The proposed method shows 3.69% improvement over zero-shot baselines and 2.08% and 3.99% improvement over the vanilla-GRPO baselines. |
ToW: Thoughts of Words Improve Reasoning in Large Language Models (2025.naacl-long)
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Zhikun Xu, Ming Shen, Jacob Dineen, Zhaonan Li, Xiao Ye, Shijie Lu, Aswin Rrv, Chitta Baral, Ben Zhou
| Challenge: | Unlike other data augmentation methods, thoughts of words (TOW) views next-word prediction as a core reasoning task and injects fine-grained thoughts into pre-training texts. |
| Approach: | They propose a training-time data-augmentation method called thoughts of words (TOW) that injects fine-grained thoughts directly into a next-word prediction task and teaches the model to understand how the observed next word is related to previous contexts. |
| Outcome: | The proposed method reduces model hallucination by 10% and improves reasoning performance by 7% to 9% on average. |
QA‐LIGN: Aligning LLMs through Constitutionally Decomposed QA (2025.findings-emnlp)
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Jacob Dineen, Aswin Rrv, Qin Liu, Zhikun Xu, Xiao Ye, Ming Shen, Zhaonan Li, Shijie Lu, Chitta Baral, Muhao Chen, Ben Zhou
| Challenge: | QA-LIGN decomposes monolithic rewards into interpretable principle-specific evaluations . scalar rewards obscure which objectives drive the training signal . |
| Approach: | a new method decomposes monolithic rewards into interpretable principle-specific evaluations . QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . |
| Outcome: | QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate . the results outperform DPO and GRPO with state-of-the-art reward models given equivalent training . |
Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter? (2024.emnlp-main)
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Nemika Tyagi, Mihir Parmar, Mohith Kulkarni, Aswin Rrv, Nisarg Patel, Mutsumi Nakamura, Arindam Mitra, Chitta Baral
| Challenge: | Existing studies evaluate only the final predicted answer of a puzzle, without providing any finer metrics to evaluate them. |
| Approach: | They propose to use a grid-based evaluation dataset to evaluate LLMs' reasoning abilities and a new error taxonomy to evaluate their reasoning chains. |
| Outcome: | The proposed model outperforms existing prompting methods on a wide range of natural language understanding tasks previously thought to be exclusive to humans. |