Demystifying the Inner Workings of Language Models

Large language models (LLMs) power a rapidly-growing and increasingly impactful suite of AI technologies. However, due to their scale and complexity, we lack a fundamental scientific understanding of much of LLMs’ behavior, even when they are open source. The “black-box” nature of LMs not only complicates model debugging and evaluation, but also limits trust and usability. In this talk, I will describe how my research on interpretability (i.e., understanding models’ inner workings) has answered key scientific questions about how models operate. I will then demonstrate how deeper insights into LLMs’ behavior enable both 1) targeted performance improvements and 2) the production of transparent, trustworthy explanations for human users.