Kernion, Jackson

In-context Learning and Induction Heads

Olsson, C., Elhage, N., Nanda, N., Joseph, N., DasSarma, N., Henighan, T., Mann, B., Askell, A., Bai, Y., Chen, A., et al. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 1,170

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 442

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

A General Language Assistant as a Laboratory for Alignment

Askell, A., Bai, Y., Chen, A., Drain, D., Ganguli, D., Henighan, T., Jones, A., Joseph, N., Mann, B., DasSarma, N., et al. (2021) · arXiv

Topic ModelingEthics and Social Impacts of AI

Cited by 146

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

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

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

Question Decomposition Improves the Faithfulness of Model-Generated Reasoning

Radhakrishnan, A., Nguyen, K., Chen, A., Chen, C., Denison, C., Hernandez, D., Durmus, E., Hubinger, E., Kernion, J., Lukošiūtė, K., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8

Specific versus General Principles for Constitutional AI

Kundu, S., Bai, Y., Kadavath, S., Askell, A., Callahan, A., Chen, A., Goldie, A., Balwit, A., Mirhoseini, A., McLean, B., et al. (2023) · arXiv

Ethics and Social Impacts of AISpeech and dialogue systems

Cited by 7