Ravfogel, Shauli

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

Ben Zaken, E., Goldberg, Y., & Ravfogel, S. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 765

Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection

Ravfogel, S., Elazar, Y., Gonen, H., Twiton, M., & Goldberg, Y. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 287

Measuring and improving consistency in pretrained language models

Elazar, Y., Kassner, N., Ravfogel, S., Ravichander, A., Hovy, E., Schütze, H., & Goldberg, Y. (2021) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 220

Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals

Elazar, Y., Ravfogel, S., Jacovi, A., & Goldberg, Y. (2021) · Transactions of the Association for Computational Linguistics

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 126

Contrastive Explanations for Model Interpretability

Jacovi, A., Swayamdipta, S., Ravfogel, S., Elazar, Y., Choi, Y., & Goldberg, Y. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 83

Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

Mosbach, M., Pimentel, T., Ravfogel, S., Klakow, D., & Elazar, Y. (2023) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 81

LEACE: Perfect linear concept erasure in closed form

Belrose, N., Schneider-Joseph, D., Ravfogel, S., Cotterell, R., Raff, E., & Biderman, S. (2023) · arXiv

Cryptography and Data SecurityPrivacy-Preserving Technologies in Data

Cited by 44

Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages

Ravfogel, S., Goldberg, Y., & Linzen, T. (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)

Natural Language Processing TechniquesTopic Modeling

Cited by 39

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

Ben-Zaken, E., Ravfogel, S., & Goldberg, Y. (2021) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 11