Jared Kaplan

According to our database¹, Jared Kaplan authored at least 42 papers between 2007 and 2025.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

In proceedings

Links

Bibliography

Reasoning Models Don't Always Say What They Think.

CoRR, May, 2025

Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations.

CoRR, March, 2025

Forecasting Rare Language Model Behaviors.

CoRR, February, 2025

Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming.

CoRR, January, 2025

Alignment faking in large language models.

Clio: Privacy-Preserving Insights into Real-World AI Use.

Sabotage Evaluations for Frontier Models.

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models.

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training.

Many-shot Jailbreaking.

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

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

Trans. Mach. Learn. Res., 2023

Evaluating and Mitigating Discrimination in Language Model Decisions.

Specific versus General Principles for Constitutional AI.

Studying Large Language Model Generalization with Influence Functions.

Measuring Faithfulness in Chain-of-Thought Reasoning.

Question Decomposition Improves the Faithfulness of Model-Generated Reasoning.

Towards Measuring the Representation of Subjective Global Opinions in Language Models.

The Capacity for Moral Self-Correction in Large Language Models.

Discovering Language Model Behaviors with Model-Written Evaluations.

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

Scaling Laws from the Data Manifold Dimension.

J. Mach. Learn. Res., 2022

Discovering Language Model Behaviors with Model-Written Evaluations.

Constitutional AI: Harmlessness from AI Feedback.

Measuring Progress on Scalable Oversight for Large Language Models.

In-context Learning and Induction Heads.

Toy Models of Superposition.

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

Language Models (Mostly) Know What They Know.

Scaling Laws and Interpretability of Learning from Repeated Data.

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

Predictability and Surprise in Large Generative Models.

Predictability and Surprise in Large Generative Models.

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

A General Language Assistant as a Laboratory for Alignment.

Evaluating Large Language Models Trained on Code.

Explaining Neural Scaling Laws.

Scaling Laws for Transfer.

Data and Parameter Scaling Laws for Neural Machine Translation.

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Scaling Laws for Autoregressive Generative Modeling.

A Neural Scaling Law from the Dimension of the Data Manifold.

Scaling Laws for Neural Language Models.

Language Models are Few-Shot Learners.

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

An Empirical Model of Large-Batch Training.

Explaining Debugging Strategies to End-User Programmers.

Proceedings of the 2007 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2007), 2007

Jared Kaplan

Timeline

Legend:

Links

On csauthors.net:

Bibliography

Loading...