In this podcast, Samuel Marks, a Postdoctoral Research Associate at Northeastern University, discusses his paper on the linear structure of true/false datasets in LLM representations. They explore how language models can linearly represent truth or falsehood, introduce a new probing technique called mass mean probing, and analyze the process of embedding truth in LLM models. They also discuss the future research directions and limitations of the paper.
