Smith, Noah A.

Demystifying Prompts in Language Models via Perplexity Estimation

Gonen, H., Iyer, S., Blevins, T., Smith, N. A., & Zettlemoyer, L. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 109

The Leaderboard Illusion

Singh, S., Nan, Y., Wang, A., D'Souza, D., Kapoor, S., Üstün, A., Koyejo, S., Deng, Y., Longpre, S., Smith, N. A., et al. (2025) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 108

Measuring Association Between Labels and Free-Text Rationales

Wiegreffe, S., Marasović, A., & Smith, N. A. (2020) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 106

Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text

Dou, Y., Forbes, M., Koncel-Kedziorski, R., Smith, N. A., & Choi, Y. (2021) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 90

Competency Problems: On Finding and Removing Artifacts in Language Data

Gardner, M., Merrill, W., Dodge, J., Peters, M., Ross, A., Singh, S., & Smith, N. A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 81

How Language Model Hallucinations Can Snowball

Zhang, M., Press, O., Merrill, W., Liu, A., & Smith, N. A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 77

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, E., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 70

Selective annotation makes language models better few-shot learners

Su, H., Kasai, J., Wu, C. H., Shi, W., Wang, T., Xin, J., Zhang, R., Ostendorf, M., Zettlemoyer, L., Smith, N. A., et al. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 70

Improving Transformer Models by Reordering their Sublayers

Press, O., Smith, N. A., & Levy, O. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 64

Shortformer: Better Language Modeling using Shorter Inputs

Press, O., Smith, N. A., & Lewis, M. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 55

Shortformer: Better language modeling using shorter inputs

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

Topic ModelingNatural Language Processing Techniques

Cited by 55

Binding Language Models in Symbolic Languages

Cheng, Z., Xie, T., Shi, P., Li, C., Nadkarni, R., Hu, Y., Xiong, C., Radev, D., Ostendorf, M., Zettlemoyer, L., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 53

Using proprietary language models in academic research requires explicit justification

Palmer, A., Smith, N. A., & Spirling, A. (2023) · Nature Computational Science

Ethics and Social Impacts of AIAcademic Publishing and Open Access

Cited by 51

Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank

Chau, E. C., Lin, L. H., & Smith, N. A. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Natural Language Processing TechniquesTopic Modeling

Cited by 44

Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?

Merrill, W., Goldberg, Y., Schwartz, R., & Smith, N. A. (2021) · Transactions of the Association for Computational Linguistics

Syntax, Semantics, Linguistic VariationLogic, Reasoning, and Knowledge

Cited by 44

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

Fine-Grained Human Feedback Gives Better Rewards for Language Model Training

Wu, Z., Hu, Y., Shi, W., Dziri, N., Suhr, A., Ammanabrolu, P., Smith, N. A., Ostendorf, M., & Hajishirzi, H. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 40

Saturated Transformers are Constant-Depth Threshold Circuits

Merrill, W., Sabharwal, A., & Smith, N. A. (2022) · Transactions of the Association for Computational Linguistics

semigroups and automata theoryComplexity and Algorithms in Graphs

Cited by 25

Choose Your Own Adventure: Paired Suggestions in Collaborative Writing for Evaluating Story Generation Models

Clark, E., & Smith, N. A. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Artificial Intelligence in GamesTopic Modeling

Cited by 23

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

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A. H., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

What's In My Big Data?

Elazar, Y., Bhagia, A., Magnusson, I., Ravichander, A., Schwenk, D., Suhr, A., Walsh, P., Groeneveld, D., Soldaini, L., Singh, S., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 9

Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2

Ivison, H., Wang, Y., Pyatkin, V., Lambert, N., Peters, M., Dasigi, P., Jang, J., Wadden, D., Smith, N. A., Beltagy, I., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 8