Llm Reasoning

A diary entry on tracing the reasoning processes of Large Language Models (LLMs) to enhance interpretability, and a discussion on the inherent difficulties and challenges in achieving AI alignment.

A diary entry on ‘Thought Anchors’, a concept for identifying key reasoning steps in Chain-of-Thought (CoT) processes that significantly influence LLM behavior, enhancing interpretability for AI safety.

A diary entry on the unfaithfulness of Chain-of-Thought (CoT) reasoning in LLMs, highlighting issues like implicit biases and logically contradictory outputs, which pose challenges for AI safety monitoring.