Interpreting Chain of Thought: A Walkthrough and Discussion

We feature an extensive discussion about **Thought Anchors**, a tool designed for interpreting the "chain of thought" within large language models (LLMs). Developed by **Paul** and **Uzzi** from **Neel Nanda's** "Neel Nanda's MATS program," the tool visualizes the sequential thoughts or "sentences" an LLM generates while solving problems, such as mathematical questions or complex scenarios involving strategic decisions like blackmail or whistleblowing. Key concepts explored include **counterfactual importance** and **resampling importance**, which measure how critical a specific sentence is to the model's final output by analyzing the impact of its alteration or removal on subsequent reasoning. The conversation also touches upon **attention suppression** for understanding direct causal links between sentences and introduces a **taxonomy** for categorizing different types of sentences generated by the LLM, aiming to provide a clearer, more navigable understanding of its internal processes.