Perez, Ethan

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv

Safety Systems Engineering in AutonomyEthics and Social Impacts of AI

Cited by 3,101

Film: Visual reasoning with a general conditioning layer

Perez, E., Strub, F., De Vries, H., Dumoulin, V., & Courville, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 2,490

Red Teaming Language Models with Language Models

Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., & Irving, G. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 363

ELI5: Long Form Question Answering

Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., & Auli, M. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 325

ELI5: long form question answering

Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., & Auli, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 319

Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting

Turpin, M., Michael, J., Perez, E., & Bowman, S. R. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 262

True Few-Shot Learning with Language Models

Perez, E., Kiela, D., & Cho, K. (2021) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 226

Discovering Language Model Behaviors with Model-Written Evaluations

Perez, E., Ringer, S., Lukosiute, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Topic ModelingNatural Language Processing Techniques

Cited by 194

Language Models (Mostly) Know What They Know

Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., Schiefer, N., Hatfield-Dodds, Z., DasSarma, N., Tran-Johnson, E., et al. (2022) · arXiv

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 187

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned

Ganguli, D., Lovitt, L., Kernion, J., Askell, A., Bai, Y., Kadavath, S., Mann, B., Perez, E., Schiefer, N., Ndousse, K., et al. (2022) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 130

Best-of-N Jailbreaking

Hughes, J., Price, S., Lynch, A., Schaeffer, R., Barez, F., Koyejo, S., Sleight, H., Jones, E., Perez, E., & Sharma, M. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 129

Unsupervised Question Decomposition for Question Answering

Perez, E., Lewis, P., Yih, W.-T., Cho, K., & Kiela, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 123

Discovering Language Model Behaviors with Model-Written Evaluations

Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2022) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 53

The Capacity for Moral Self-Correction in Large Language Models

Ganguli, D., Askell, A., Schiefer, N., Liao, T. I., Lukošiūtė, K., Chen, A., Goldie, A., Mirhoseini, A., Olsson, C., Hernandez, D., et al. (2023) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 53

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training

Hubinger, E., Denison, C., Mu, J., Lambert, M., Tong, M., MacDiarmid, M., Lanham, T., Ziegler, D. M., Maxwell, T., Cheng, N., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 46

Measuring Progress on Scalable Oversight for Large Language Models

Bowman, S. R., Hyun, J., Perez, E., Chen, E., Pettit, C., Heiner, S., Lukošiūtė, K., Askell, A., Jones, A., Chen, A., et al. (2022) · arXiv

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 34

Measuring Faithfulness in Chain-of-Thought Reasoning

Lanham, T., Chen, A., Radhakrishnan, A., Steiner, B., Denison, C., Hernandez, D., Li, D., Durmus, E., Hubinger, E., Kernion, J., et al. (2023) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 33

Alignment faking in large language models

Greenblatt, R., Denison, C., Wright, B., Roger, F., MacDiarmid, M., Marks, S., Treutlein, J., Belonax, T., Chen, J., Duvenaud, D., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 30

Inverse Scaling: When Bigger Isn't Better

McKenzie, I. R., Lyzhov, A., Pieler, M., Parrish, A., Mueller, A., Prabhu, A., McLean, E., Kirtland, A., Ross, A., Liu, A., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 29

Finding Generalizable Evidence by Learning to Convince Q&A Models

Perez, E., Karamcheti, S., Fergus, R., Weston, J., Kiela, D., & Cho, 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 27

Studying Large Language Model Generalization with Influence Functions

Grosse, R., Bae, J., Anil, C., Elhage, N., Tamkin, A., Tajdini, A., Steiner, B., Li, D., Durmus, E., Perez, E., et al. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine Learning

Cited by 26

Many-shot Jailbreaking

Anil, C., Durmus, E., Panickssery, N., Sharma, M., Benton, J., Kundu, S., Batson, J., Tong, M., Mu, J., Ford, D., et al. (2024) · Advances in Neural Information Processing Systems 37

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 25

Pretraining Language Models with Human Preferences

Korbak, T., Shi, K., Chen, A., Bhalerao, R., Buckley, C. L., Phang, J., Bowman, S. R., & Perez, E. (2023) · arXiv

Machine Learning and AlgorithmsInnovative Teaching and Learning Methods

Cited by 23

Looking Inward: Language Models Can Learn About Themselves by Introspection

Binder, F. J., Chua, J., Korbak, T., Sleight, H., Hughes, J., Long, R., Perez, E., Turpin, M., & Evans, O. (2024) · arXiv

Explainable Artificial Intelligence (XAI)Philosophy and Theoretical Science

Cited by 20