In this episode, we dive deep into the inner workings of large language models with Professor David Bau and grad students Koyena Pal and Eric Todd from Northeastern University. Koyena shares insights from her Future Lens paper, which shows that even mid-sized language models think multiple tokens ahead. Eric discusses the fascinating concept of Function Vectors - complex patterns of activity spread across transformer layers that enable in-context learning. Professor Bau connects the dots between these projects and the lab's broader interpretability research agenda, identifying key abstractions that link low-level computations to higher-level model behaviors.
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A weekly podcast where hosts Erik Torenberg and Nathan Labenz interview the builders on the edge of AI and explore the dramatic shift it will unlock in the coming years.
