Deep Papers is a podcast series featuring deep dives on today’s seminal AI papers and research. Hosted by AI Pub creator Brian Burns and Arize AI founders Jason Lopatecki and Aparna Dhinakaran, each episode profiles the people and techniques behind cutting-edge breakthroughs in machine learning. In this episode, we interview Timo Schick and Thomas Scialom, the Research Scientists at Meta AI behind Toolformer. "Vanilla" language models cannot access information about the external world. But what if we gave language models access to calculators, question-answer search, and other APIs to generate more powerful and accurate output? Further, how do we train such a model? How can we automatically generate a dataset of API-call-annotated text at internet scale, without human labeling? Timo and Thomas give a step-by-step walkthrough of building and training Toolformer, what motivated them to do it, and what we should expect in the next generation of tool-LLM powered products. Follow AI__Pub on Twitter. To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter. Follow AI__Pub on Twitter. To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.