Ethayarajh, Kawin

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings

Ethayarajh, K. (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 841

Utility is in the Eye of the User: A Critique of NLP Leaderboards

Ethayarajh, K., & Jurafsky, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 147

KTO: Model Alignment as Prospect Theoretic Optimization

Ethayarajh, K., Xu, W., Muennighoff, N., Jurafsky, D., & Kiela, D. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 29

Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds

Ethayarajh, K. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 12

The Authenticity Gap in Human Evaluation

Ethayarajh, K., & Jurafsky, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 10

Anchor Points: Benchmarking Models with Much Fewer Examples

Vivek, R., Ethayarajh, K., Yang, D., & Kiela, D. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 4

Anchor Points: Benchmarking Models with Much Fewer Examples

Vivek, R., Ethayarajh, K., Yang, D., & Kiela, D. (2024) · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 4