Williams, Adina

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

Llama 2: Open Foundation and Fine-Tuned Chat Models

Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 2,604

Adversarial NLI: A New Benchmark for Natural Language Understanding

Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., & Kiela, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 656

Dynabench: Rethinking Benchmarking in NLP

Kiela, D., Bartolo, M., Nie, Y., Kaushik, D., Geiger, A., Wu, Z., Vidgen, B., Prasad, G., Singh, A., Ringshia, P., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 254

“I’m sorry to hear that”: Finding new biases in language models with a holistic descriptor dataset

Smith, E. M., Hall, M., Kambadur, M., Presani, E., & Williams, A. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 97

Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora

Warstadt, A., Mueller, A., Choshen, L., Wilcox, E., Zhuang, C., Ciro, J., Mosquera, R., Paranjabe, B., Williams, A., Linzen, T., et al. (2023) · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning

Topic ModelingNatural Language Processing Techniques

Cited by 74

Perturbation augmentation for fairer NLP

Qian, R., Ross, C., Fernandes, J., Smith, E. M., Kiela, D., & Williams, A. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Ethics and Social Impacts of AITopic Modeling

Cited by 69

Brittlebench: Quantifying LLM robustness via prompt sensitivity

Romanou, A., Ibrahim, M., Ross, C., Oktar, K., Shaib, C., Bell, S. J., Ovalle, A., Bosselut, A., Dodge, J., Sinha, K., et al. (2026) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 24

Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus

Warstadt, A., Choshen, L., Mueller, A., Williams, A., Wilcox, E., & Zhuang, C. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 14

Towards Geographic Inclusion in the Evaluation of Text-to-Image Models

Hall, M., Bell, S. J., Ross, C., Williams, A., Drozdzal, M., & Soriano, A. R. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency

Generative Adversarial Networks and Image SynthesisEthics and Social Impacts of AI

Cited by 11

Bigger is not always better: The importance of human-scale language modeling for psycholinguistics

Wilcox, E. G., Hu, M. Y., Mueller, A., Warstadt, A., Choshen, L., Zhuang, C., Williams, A., Cotterell, R., & Linzen, T. (2025) · Journal of Memory and Language

Natural Language Processing TechniquesTopic Modeling

Cited by 9

Sometimes We Want Ungrammatical Translations

Parthasarathi, P., Sinha, K., Pineau, J., & Williams, A. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021

Natural Language Processing TechniquesTopic Modeling

Cited by 9

Introducing v0.5 of the AI Safety Benchmark from MLCommons

Vidgen, B., Agrawal, A., Ahmed, A. M., Akinwande, V., Al-Nuaimi, N., Alfaraj, N., Alhajjar, E., Aroyo, L., Bavalatti, T., Bartolo, M., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 5

DataPerf: Benchmarks for Data-Centric AI Development

Mazumder, M., Banbury, C., Yao, X., Karlaš, B., Rojas, W. G., Diamos, S., Diamos, G., He, L., Parrish, A., Kirk, H. R., et al. (2023) · Advances in Neural Information Processing Systems 36

Advanced Database Systems and QueriesScientific Computing and Data Management

Cited by 5