Events

AI models often learn problematic reasoning processes due to misspecified training objectives. Interpretability helps us detect, and often fix, such reasoning. For example, inspecting Chain-of-Thought reasoning in LLMs is perhaps the single most common approach to understanding how a model got to its answer. This practice has proven effective for identifying model reasoning failures, mistaken background knowledge, and misinterpretation of user instructions. Yet whether Chain-of-Thought is a faithful reflection of a model’s true reasoning remains a subject of debate. On this point, I present work on the CoT faithfulness problem, including evaluations for explanation faithfulness and methods for improving the faithfulness of CoT explanations. Process supervision, and not merely outcome supervision, significantly improves CoT faithfulness, opening up important applications in monitoring model reasoning for safety. From here, I argue that in order to obtain a complete picture of model interpretability, we must also sharpen our understanding of how internal model representations drive external behavior. I show that, by determining how models represent knowledge, we can control what facts are encoded in models and detect when they output claims that they know are untrue or misleading. With more faithful textual reasoning and better interpretability of model representations, we will be able to efficiently identify and fix safety failures in LLMs.