Reasoning with interactive guidance
Thursday, October 05, 2023
Humans view AI as a tool that listens and learns from their interactions, but this differs from the standard train-test paradigm. The goal of this talk is to introduce a step towards bridging this gap by enabling large language models to focus on human needs and continuously learn. Drawing inspiration from the theory of recursive reminding in Psychology, we propose a memory architecture to guide models to avoid repeating past errors. The talk discusses four essential research questions: who to ask, what to ask, when to ask and how to apply the obtained guidance. In tasks such as moral reasoning, planning, and other reasoning as well as benchmark tasks, our approach enables models to improve with reflection.
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Speaker/s
Niket Tandon is a Senior Research Scientist at the Allen Institute for AI in Seattle. His research interests are in commonsense reasoning and natural language guided reasoning. He works at the Aristo team responsible for creating AI which aced science exams. He obtained his Ph.D. from the Max Planck Institute for Informatics in Germany in 2016, where he was supervised by Professor Gerhard Weikum, resulting in the largest automatically extracted commonsense knowledge base at the time, called WebChild. He is also the founder of PQRS research, which introduces undergraduate students from underrepresented institutes to AI research.
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