Bowman, Samuel R.

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Topic ModelingNatural Language Processing Techniques

Cited by 4,222

A large annotated corpus for learning natural language inference

Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 3,377

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Williams, A., Nangia, N., & Bowman, S. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 3,342

Generating Sentences from a Continuous Space

Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., & Bengio, S. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning

Topic ModelingSpeech Recognition and Synthesis

Cited by 2,061

Neural Network Acceptability Judgments

Warstadt, A., Singh, A., & Bowman, S. R. (2019) · Transactions of the Association for Computational Linguistics

Natural Language Processing TechniquesTopic Modeling

Cited by 1,215

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,051

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

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

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2018) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 562

LLM Evaluators Recognize and Favor Their Own Generations

Panickssery, A., Bowman, S. R., & Feng, S. (2024) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 559

On Measuring Social Biases in Sentence Encoders

May, C., Wang, A., Bordia, S., Bowman, S. R., & Rudinger, R. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Ethics and Social Impacts of AITopic Modeling

Cited by 461

CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models

Nangia, N., Vania, C., Bhalerao, R., & Bowman, S. R. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 446

Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting

Turpin, M., Michael, J., Perez, E., & Bowman, S. R. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 262

Identifying and Reducing Gender Bias in Word-Level Language Models

Bordia, S., & Bowman, S. R. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

Ethics and Social Impacts of AITopic Modeling

Cited by 219

Discovering Language Model Behaviors with Model-Written Evaluations

Perez, E., Ringer, S., Lukosiute, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Topic ModelingNatural Language Processing Techniques

Cited by 194

BBQ: A Hand-Built Bias Benchmark for Question Answering

Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S. R. (2021) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 189

BBQ: A hand-built bias benchmark for question answering

Parrish, A., Chen, A., Nangia, N., Padmakumar, V., Phang, J., Thompson, J., Htut, P. M., & Bowman, S. (2022) · Findings of the Association for Computational Linguistics: ACL 2022

Ethics and Social Impacts of AITopic Modeling

Cited by 186

Eight Things to Know about Large Language Models

Bowman, S. R. (2023) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 137

What Artificial Neural Networks Can Tell Us about Human Language Acquisition

Warstadt, A., & Bowman, S. R. (2022) · Algebraic Structures in Natural Language

Language Development and DisordersNatural Language Processing Techniques

Cited by 111

Discourse-Based Objectives for Fast Unsupervised Sentence Representation Learning

Jernite, Y., Bowman, S. R., & Sontag, D. (2017) · arXiv

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 101

When Do You Need Billions of Words of Pretraining Data?

Zhang, Y., Warstadt, A., Li, X., & Bowman, S. R. (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 85

Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training

Hosseini, E. A., Schrimpf, M., Zhang, Y., Bowman, S., Zaslavsky, N., & Fedorenko, E. (2024) · Neurobiology of Language

Neurobiology of Language and BilingualismLanguage Development and Disorders

Cited by 63

Discovering Language Model Behaviors with Model-Written Evaluations

Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2022) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 53