Interpretability in Language Models

The podcast episode delves into Peter Hossie's research areas during his PhD, highlighting three key focuses. Firstly, interpretability deals with understanding language models' internal reasoning processes, emphasizing the importance of trust in these models' responses. Secondly, model editing aims to update factual knowledge within language models, revealing challenges in pinpointing where information is stored within the model. Lastly, scalable oversight addresses supervising AI systems as they improve at tasks, where understanding model interpretability can enhance overall safety measures.