Vidgen, Bertie

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

Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection

Vidgen, B., Thrush, T., Waseem, Z., & Kiela, D. (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)

Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion Mining

Cited by 174

The benefits, risks and bounds of personalizing the alignment of large language models to individuals

Kirk, H. R., Vidgen, B., Röttger, P., & Hale, S. A. (2024) · Nature Machine Intelligence

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 122

XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

Röttger, P., Kirk, H., Vidgen, B., Attanasio, G., Bianchi, F., & Hovy, D. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 77

Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Kirk, H. R., Vidgen, B., Röttger, P., & Hale, S. A. (2023) · arXiv

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 22

FinanceBench: A New Benchmark for Financial Question Answering

Islam, P., Kannappan, A., Kiela, D., Qian, R., Scherrer, N., & Vidgen, B. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 16

XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

Röttger, P., Kirk, H. R., Vidgen, B., Attanasio, G., Bianchi, F., & Hovy, D. (2023) · arXiv

Adversarial Robustness in Machine LearningHate Speech and Cyberbullying Detection

Cited by 6

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

Near to Mid-term Risks and Opportunities of Open-Source Generative AI

Eiras, F., Petrov, A., Vidgen, B., de Witt, C. S., Pizzati, F., Elkins, K., Mukhopadhyay, S., Bibi, A., Csaba, B., Steibel, F., et al. (2024) · arXiv

Ethics and Social Impacts of AILaw, AI, and Intellectual Property

Cited by 2