Prabhumoye, Shrimai

Self-Refine: Iterative Refinement with Self-Feedback

Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2,314

The Second Conversational Intelligence Challenge (ConvAI2)

Dinan, E., Logacheva, V., Malykh, V., Miller, A., Shuster, K., Urbanek, J., Kiela, D., Szlam, A., Serban, I., Lowe, R., et al. (2019) · arXiv

Speech and dialogue systemsAI in Service Interactions

Cited by 355

Style transfer through back-translation

Prabhumoye, S., Tsvetkov, Y., Salakhutdinov, R., & Black, A. W. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Generative Adversarial Networks and Image SynthesisAuthorship Attribution and Profiling

Cited by 348

Five sources of bias in natural language processing

Hovy, D., & Prabhumoye, S. (2021) · Language and Linguistics Compass

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 298

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Smith, S., Patwary, M., Norick, B., LeGresley, P., Rajbhandari, S., Casper, J., Liu, Z., Prabhumoye, S., Zerveas, G., Korthikanti, V., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 286

Nemotron-4 340B Technical Report

Nvidia, :, Adler, B., Agarwal, N., Aithal, A., Anh, D. H., Bhattacharya, P., Brundyn, A., Casper, J., Catanzaro, B., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 66

Equity Beyond Bias in Language Technologies for Education

Mayfield, E., Madaio, M., Prabhumoye, S., Gerritsen, D., McLaughlin, B., Dixon-Román, E., & Black, A. W. (2019) · Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 62

Nemotron-4 15B Technical Report

Parmar, J., Prabhumoye, S., Jennings, J., Patwary, M., Subramanian, S., Su, D., Zhu, C., Narayanan, D., Jhunjhunwala, A., Dattagupta, A., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 9

Plan, Eliminate, and Track - Language Models are Good Teachers for Embodied Agents

Wu, Y., Min, S. Y., Bisk, Y., Salakhutdinov, R., Azaria, A., Li, Y., Mitchell, T., & Prabhumoye, S. (2023) · arXiv

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 7

Llama-Nemotron: Efficient Reasoning Models

Bercovich, A., Levy, I., Golan, I., Dabbah, M., El-Yaniv, R., Puny, O., Galil, I., Moshe, Z., Ronen, T., Nabwani, N., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1

Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models

NVIDIA, :, Blakeman, A., Basant, A., Khattar, A., Renduchintala, A., Bercovich, A., Ficek, A., Bjorlin, A., Taghibakhshi, A., et al. (2025) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 0