Smith, Noah A.

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

Green AI

Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020) · Communications of the ACM

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 1,647

Self-Instruct: Aligning Language Models with Self-Generated Instructions

Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., & Hajishirzi, H. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 1,243

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

The Risk of Racial Bias in Hate Speech Detection

Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Hate Speech and Cyberbullying DetectionEthics and Social Impacts of AI

Cited by 817

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

Is Attention Interpretable?

Serrano, S., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 690

Knowledge Enhanced Contextual Word Representations

Peters, M. E., Neumann, M., Logan, R., Schwartz, R., Joshi, V., Singh, S., & 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)

Topic ModelingNatural Language Processing Techniques

Cited by 661

Recurrent Neural Network Grammars

Dyer, C., Kuncoro, A., Ballesteros, M., & Smith, N. A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Natural Language Processing TechniquesSyntax, Semantics, Linguistic Variation

Cited by 517

To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks

Peters, M. E., Ruder, S., & Smith, N. A. (2019) · Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 432

Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Dodge, J., Ilharco, G., Schwartz, R., Farhadi, A., Hajishirzi, H., & Smith, N. (2020) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 412

Measuring and Narrowing the Compositionality Gap in Language Models

Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N., & Lewis, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Topic ModelingNatural Language Processing Techniques

Cited by 303

Measuring and Narrowing the Compositionality Gap in Language Models

Press, O., Zhang, M., Min, S., Schmidt, L., Smith, N. A., & Lewis, M. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 294

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

Creative Writing with a Machine in the Loop: Case Studies on Slogans and Stories

Clark, E., Ross, A. S., Tan, C., Ji, Y., & Smith, N. A. (2018) · 23rd International Conference on Intelligent User Interfaces

Natural Language Processing TechniquesArtificial Intelligence in Games

Cited by 257

Measuring the Carbon Intensity of AI in Cloud Instances

Dodge, J., Prewitt, T., Tachet des Combes, R., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A. S., Smith, N. A., DeCario, N., & Buchanan, W. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 257

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

Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

Press, O., Smith, N. A., & Lewis, M. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 161

A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N. A., & Gardner, M. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingInformation Retrieval and Search Behavior

Cited by 138

Measuring Association Between Labels and Free-Text Rationales

Wiegreffe, S., Marasović, A., & Smith, N. A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 119

Is GPT-3 text indistinguishable from human text? Scarecrow: A framework for scrutinizing machine text

Dou, Y., Forbes, M., Koncel-Kedziorski, R., Smith, N., & Choi, Y. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 118

Self-Instruct: Aligning Language Models with Self-Generated Instructions

Wang, Y., Kordi, Y., Mishra, S., Liu, A., Smith, N. A., Khashabi, D., & Hajishirzi, H. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 118

Demystifying Prompts in Language Models via Perplexity Estimation

Gonen, H., Iyer, S., Blevins, T., Smith, N., & Zettlemoyer, L. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Topic ModelingNatural Language Processing Techniques

Cited by 109