LLM Interpretability
- is the field focused on understanding the internal workings of large language models (LLMs) to reveal their reasoning processes, decision-making mechanisms, and the concepts they use to generate responses
- use techniques like mechanistic interpretability to map features to internal components, aiming to improve AI safety, fairness, alignment, and the reliability of LLMs by moving them from “black boxes” to more transparent systems