Gururangan, Suchin

Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks

Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 2,103

Annotation Artifacts in Natural Language Inference Data

Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., & Smith, N. A. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 907

RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models

Gehman, S., Gururangan, S., Sap, M., Choi, Y., & Smith, N. A. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Hate Speech and Cyberbullying DetectionAdversarial Robustness in Machine Learning

Cited by 693

All That’s ‘Human’ Is Not Gold: Evaluating Human Evaluation of Generated Text

Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., & Smith, N. A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 290

All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text

Clark, E., August, T., Serrano, S., Haduong, N., Gururangan, S., & Smith, N. A. (2021) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 290

Show Your Work: Improved Reporting of Experimental Results

Dodge, J., Gururangan, S., Card, D., Schwartz, R., & Smith, N. A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Academic Writing and PublishingScientific Computing and Data Management

Cited by 233

Editing Models with Task Arithmetic

Ilharco, G., Ribeiro, M. T., Wortsman, M., Gururangan, S., Schmidt, L., Hajishirzi, H., & Farhadi, A. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 66

Time Waits for No One! Analysis and Challenges of Temporal Misalignment

Luu, K., Khashabi, D., Gururangan, S., Mandyam, K., & Smith, N. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Natural Language Processing TechniquesTopic Modeling

Cited by 41

Show Your Work: Improved Reporting of Experimental Results

Dodge, J., Gururangan, S., Card, D., Schwartz, R., & Smith, N. A. (2019) · arXiv

Machine Learning and Data ClassificationNatural Language Processing Techniques

Cited by 22

Language models scale reliably with over-training and on downstream tasks

Gadre, S. Y., Smyrnis, G., Shankar, V., Gururangan, S., Wortsman, M., Shao, R., Mercat, J., Fang, A., Li, J., Keh, S., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 5

Rethinking Thinking Tokens: LLMs as Improvement Operators

Madaan, L., Didolkar, A., Gururangan, S., Quan, J., Silva, R., Salakhutdinov, R., Zaheer, M., Arora, S., & Goyal, A. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0