Papers by Ashwin Kalyan
QualEval: Qualitative Evaluation for Model Improvement (2024.naacl-long)
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Vishvak Murahari, Ameet Deshpande, Peter Clark, Tanmay Rajpurohit, Ashish Sabharwal, Karthik Narasimhan, Ashwin Kalyan
| Challenge: | Quantitative evaluation metrics are inadequate for large language models due to complexity of tasks and cannot provide actionable diagnostics. |
| Approach: | They propose a quantitative evaluation tool called QualEval that uses automated qualitative evaluation as a vehicle for model improvement. |
| Outcome: | The proposed method improves the performance of the Llama 2 model by 15% compared to baselines. |
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. |
Toxicity in chatgpt: Analyzing persona-assigned language models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing community. |
| Approach: | They evaluate toxicity in over half a million generations of ChatGPT by assigning it a persona . they find that outputs engage in incorrect stereotypes, harmful dialogue, hurtful opinions . |
| Outcome: | a new study shows that assigning a persona to a chatbot can increase toxicity in half a million generations. |
How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI (2021.emnlp-main)
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| Challenge: | a new reasoning challenge is proposed to help AI systems to solve real-world problems . Fermi Problems are questions whose answers can only be approximated because their computation is either impossible or impossible. |
| Approach: | They propose a new reasoning challenge, Fermi Problems, which asks questions whose answers can only be approximated because their computation is either impractical or impossible. |
| Outcome: | The proposed datasets show that even fine-tuned large-scale language models perform poorly on these datasets. |
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation (2023.emnlp-main)
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| Challenge: | Existing approaches for distilling large language models into smaller, more efficient student models are based on educational science principles such as knowledge tracing and personalized learning. |
| Approach: | They propose a method for distilling large language models into smaller, more efficient student models that are aligned with educational science principles such as knowledge tracing and personalized learning. |
| Outcome: | The proposed approach outperforms LLMs on three benchmarks while employing significantly fewer parameters. |
LILA: A Unified Benchmark for Mathematical Reasoning (2022.emnlp-main)
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Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, Ashwin Kalyan
| Challenge: | Towards evaluating and improving AI systems in this domain, we propose a mathematical reasoning benchmark based on 23 diversetasks . |
| Approach: | They propose a mathematical reasoning benchmark that includes 23 diverse tasks . they extend the benchmark by collecting task instructions and solutions in the form of Python programs . |
| Outcome: | The proposed model improves on multi-tasking while the best performing model only achieves 60.40%. |
NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks (2022.acl-long)
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Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Sachdeva, Peter Clark, Chitta Baral, Ashwin Kalyan
| Challenge: | Existing AI systems fail to perform basic mathematical reasoning when presented in a slightly different manner. |
| Approach: | They propose a multi-task benchmark that evaluates the performance of AI systems on eight different tasks that at their core require simple arithmetic understanding. |
| Outcome: | The proposed benchmark compares the performance of AI systems on eight different tasks. |
PersonaGym: Evaluating Persona Agents and LLMs (2025.findings-emnlp)
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Vinay Samuel, Henry Peng Zou, Yue Zhou, Shreyas Chaudhari, Ashwin Kalyan, Tanmay Rajpurohit, Ameet Deshpande, Karthik R Narasimhan, Vishvak Murahari
| Challenge: | Persona agents are LLM agents conditioned to act according to an assigned persona . evaluating how faithfully these agents adhere to their personas remains a challenge . |
| Approach: | a new study evaluates persona agents' ability to act according to an assigned persona . a persona agent's person score is a human-aligned automatic metric that can be used to evaluate a model . |
| Outcome: | a new evaluation framework and a human-aligned automatic metric show that persona agents can perform better. |
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 . |
RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs (2023.acl-long)
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| Challenge: | Despite their success, even the largest language models make mistakes. |
| Approach: | They propose a framework where one language model can generate critiques to improve its peer's performance. |
| Outcome: | The proposed framework improves the performance of a fixed model 200 times its size by 10% over other models. |
C-STS: Conditional Semantic Textual Similarity (2023.emnlp-main)
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Ameet Deshpande, Carlos Jimenez, Howard Chen, Vishvak Murahari, Victoria Graf, Tanmay Rajpurohit, Ashwin Kalyan, Danqi Chen, Karthik Narasimhan
| Challenge: | Semantic textual similarity (STS) is a cornerstone task in natural language processing, but it is inherently ambiguous. |
| Approach: | They propose a task called conditional STS which measures similarity conditioned on an aspect elucidated in natural language. |
| Outcome: | The proposed task reduces subjectivity and ambiguity and enables fine-grained similarity evaluation using diverse conditions. |