Discussion of Research Paper

Discussion of the paper on Mechanistic Interpretability - Identifying Human-Interpretable Concepts and Algorithms in LLMs. Read some of the AI headlines from the past few weeks. If you still believe that interpretability and enhancing our understanding of LLMs is not important, read them again. In general, mechanistic interpretability aims to discover, understand, and verify the algorithms that model weights implement by reverse engineering model computation into components comprehensible to humans. Specifically, we will examine the Interpretability in the Wild paper in this session (https://openreview.net/pdf?id=NpsVSN6o4ul). It identifies a circuit for indirect object identification in the small GPT-2 model and has served as an influential real-world proof of concept in the field. Before the session, please read the paper (https://openreview.net/pdf?id=NpsVSN6o4ul). The more questions you have after reading, the more productive the session will be! You can also take a look at this more practical interpretability course chapter on the same paper: https://learn.arena.education/chapter1_transformer_interp/21_ioi/intro.