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Why We Think

Special thanks to John Schulman for a lot of super valuable feedback and direct edits on this post. Test time compute (Graves et al. 2016, Ling, et al. 2017, Cobbe et al. 2021) and Chain-of-thought (CoT) (W...

Date: May 1, 2025|Estimated Reading Time: 40 min|Author: Lilian Weng

Reward Hacking in Reinforcement Learning

Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards, without genuinely learning or completing the intended task....

Date: November 28, 2024|Estimated Reading Time: 37 min|Author: Lilian Weng

Extrinsic Hallucinations in LLMs

Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to case...

Date: July 7, 2024|Estimated Reading Time: 29 min|Author: Lilian Weng

Prompt Engineering Guide

A comprehensive guide to prompt engineering for large language models, covering zero-shot, few-shot, chain-of-thought, and advanced techniques for getting the best results from modern AI systems.

Date: March 15, 2023|Estimated Reading Time: 21 min|Author: Lilian Weng