Boyd-Graber, Jordan

No Explainability without Accountability: An Empirical Study of Explanations and Feedback in Interactive ML

Smith-Renner, A., Fan, R., Birchfield, M., Wu, T., Boyd-Graber, J., Weld, D. S., & Findlater, L. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 112

Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback

Nguyen, K., Daumé III, H., & Boyd-Graber, J. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesReinforcement Learning in Robotics

Cited by 110

What can AI do for me?: evaluating machine learning interpretations in cooperative play

Feng, S., & Boyd-Graber, J. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 92

Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs Through a Global Prompt Hacking Competition

Schulhoff, S., Pinto, J., Khan, A., Bouchard, L.-F., Si, C., Anati, S., Tagliabue, V., Kost, A., Carnahan, C., & Boyd-Graber, J. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 54

Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?

Rodriguez, P., Barrow, J., Hoyle, A. M., Lalor, J. P., Jia, R., & Boyd-Graber, J. (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)

Natural Language Processing TechniquesTopic Modeling

Cited by 47

It Takes Two to Lie: One to Lie, and One to Listen

Peskov, D., Cheng, B., Elgohary, A., Barrow, J., Danescu-Niculescu-Mizil, C., & Boyd-Graber, J. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Misinformation and Its ImpactsHate Speech and Cyberbullying Detection

Cited by 30

Large Language Models Help Humans Verify Truthfulness – Except When They Are Convincingly Wrong

Si, C., Goyal, N., Wu, T., Zhao, C., Feng, S., Daumé Iii, H., & Boyd-Graber, J. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Misinformation and Its ImpactsTopic Modeling

Cited by 26

Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions

Wallace, E., & Boyd-Graber, J. (2018) · Proceedings of ACL 2018, Student Research Workshop

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 14

Program Chairs’ Report on Peer Review at ACL 2023

Rogers, A., Karpinska, M., Boyd-Graber, J., & Okazaki, N. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Publishing and Scholarly CommunicationAcademic Writing and Publishing

Cited by 5

Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition

Schulhoff, S., Pinto, J., Khan, A., Bouchard, L.-F., Si, C., Anati, S., Tagliabue, V., Kost, A. L., Carnahan, C., & Boyd-Graber, J. (2023) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 4