Padmakumar, Vishakh

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

BBQ: A Hand-Built Bias Benchmark for Question Answering

Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S. R. (2021) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 189

BBQ: A hand-built bias benchmark for question answering

Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S. (2022) · Findings of the Association for Computational Linguistics: ACL 2022

Ethics and Social Impacts of AITopic Modeling

Cited by 186

Creativity Support in the Age of Large Language Models: An Empirical Study Involving Professional Writers

Chakrabarty, T., Padmakumar, V., Brahman, F., & Muresan, S. (2024) · Creativity and Cognition

Creativity in Education and NeuroscienceNatural Language Processing Techniques

Cited by 61

QuALITY: Question Answering with Long Input Texts, Yes!

Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 51

Does Writing with Language Models Reduce Content Diversity?

Padmakumar, V., & He, H. (2023) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 21

QuALITY: Question Answering with Long Input Texts, Yes!

Pang, R. Y., Parrish, A., Joshi, N., Nangia, N., Phang, J., Chen, A., Padmakumar, V., Ma, J., Thompson, J., He, H., et al. (2021) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 10

Debate Helps Supervise Unreliable Experts

Michael, J., Mahdi, S., Rein, D., Petty, J., Dirani, J., Padmakumar, V., & Bowman, S. R. (2023) · arXiv

Epistemology, Ethics, and MetaphysicsExpert finding and Q&A systems

Cited by 8

Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs

Chen, A., Phang, J., Parrish, A., Padmakumar, V., Zhao, C., Bowman, S. R., & Cho, K. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7

Machine-in-the-Loop Rewriting for Creative Image Captioning

Padmakumar, V., & He, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Natural Language Processing TechniquesSpeech and dialogue systems

Cited by 7

Reward Gaming in Conditional Text Generation

Pang, R. Y., Padmakumar, V., Sellam, T., Parikh, A. P., & He, H. (2022) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 2

Evaluating the Diversity and Quality of LLM Generated Content

Shypula, A., Li, S., Zhang, B., Padmakumar, V., Yin, K., & Bastani, O. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1