Pavlick, Ellie

BERT Rediscovers the Classical NLP Pipeline

Tenney, I., Das, D., & Pavlick, E. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 1,307

Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference

McCoy, R. T., Pavlick, E., & Linzen, T. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 944

What do you learn from context? Probing for sentence structure in contextualized word representations

Tenney, I., Xia, P., Chen, B., Wang, A., Poliak, A., McCoy, R. T., Kim, N., Van Durme, B., Bowman, S. R., Das, D., et al. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 698

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

Inherent Disagreements in Human Textual Inferences

Pavlick, E., & Kwiatkowski, T. (2019) · Transactions of the Association for Computational Linguistics

Natural Language Processing TechniquesTopic Modeling

Cited by 214

Do Prompt-Based Models Really Understand the Meaning of Their Prompts?

Webson, A., & Pavlick, E. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 203

Measuring and Reducing Gendered Correlations in Pre-trained Models

Webster, K., Wang, X., Tenney, I., Beutel, A., Pitler, E., Pavlick, E., Chen, J., Chi, E., & Petrov, S. (2020) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 195

Symbols and grounding in large language models

Pavlick, E. (2023) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

Natural Language Processing TechniquesTopic Modeling

Cited by 95

Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color

Abdou, M., Kulmizev, A., Hershcovich, D., Frank, S., Pavlick, E., & Søgaard, A. (2021) · arXiv

Categorization, perception, and languageTopic Modeling

Cited by 70

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color

Abdou, M., Kulmizev, A., Hershcovich, D., Frank, S., Pavlick, E., & Søgaard, A. (2021) · Proceedings of the 25th Conference on Computational Natural Language Learning

Categorization, perception, and languageTopic Modeling

Cited by 64

The MultiBERTs: BERT Reproductions for Robustness Analysis

Sellam, T., Yadlowsky, S., Wei, J., Saphra, N., D'Amour, A., Linzen, T., Bastings, J., Turc, I., Eisenstein, J., Das, D., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 37

Linearly Mapping from Image to Text Space

Merullo, J., Castricato, L., Eickhoff, C., & Pavlick, E. (2022) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 34

Does Vision-and-Language Pretraining Improve Lexical Grounding?

Yun, T., Sun, C., & Pavlick, E. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 22

AND does not mean OR: Using Formal Languages to Study Language Models’ Representations

Traylor, A., Feiman, R., & Pavlick, E. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 19

Unit Testing for Concepts in Neural Networks

Lovering, C., & Pavlick, E. (2022) · Transactions of the Association for Computational Linguistics

Categorization, perception, and languageNeural Networks and Applications

Cited by 12

“Was it “stated” or was it “claimed”?: How linguistic bias affects generative language models

Patel, R., & Pavlick, E. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 11

Lessons from the Trenches on Reproducible Evaluation of Language Models

Biderman, S., Schoelkopf, H., Sutawika, L., Gao, L., Tow, J., Abbasi, B., Aji, A. F., Ammanamanchi, P. S., Black, S., Clive, J., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 9

Break It Down: Evidence for Structural Compositionality in Neural Networks

Lepori, M. A., Serre, T., & Pavlick, E. (2023) · arXiv

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 4