Decoding AI's Intent: New Methods for Understanding LLM Actions

This research offers exciting new techniques to understand the motivations behind a Large Language Model's (LLM) actions. By investigating potentially concerning behaviors, like cheating, the study aims to differentiate between accidental errors and malicious intent, paving the way for more reliable and trustworthy AI systems. The innovative approach focuses on the crucial first step of reading the Chain of Thought to understand an LLM’s decision-making process.