Representational Alignment Supports Effective Machine Teaching

• URL: http://arxiv.org/abs/2406.04302v1
• Date: Thu, 6 Jun 2024 17:48:24 GMT
• Title: Representational Alignment Supports Effective Machine Teaching
• Authors: Ilia Sucholutsky, Katherine M. Collins, Maya Malaviya, Nori Jacoby, Weiyang Liu, Theodore R. Sumers, Michalis Korakakis, Umang Bhatt, Mark Ho, Joshua B. Tenenbaum, Brad Love, Zachary A. Pardos, Adrian Weller, Thomas L. Griffiths,
• Abstract summary: We integrate insights from machine teaching and pragmatic communication with the literature on representational alignment. We design a supervised learning environment that disentangles representational alignment from teacher accuracy.
• Score: 81.19197059407121
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A good teacher should not only be knowledgeable; but should be able to communicate in a way that the student understands -- to share the student's representation of the world. In this work, we integrate insights from machine teaching and pragmatic communication with the burgeoning literature on representational alignment to characterize a utility curve defining a relationship between representational alignment and teacher capability for promoting student learning. To explore the characteristics of this utility curve, we design a supervised learning environment that disentangles representational alignment from teacher accuracy. We conduct extensive computational experiments with machines teaching machines, complemented by a series of experiments in which machines teach humans. Drawing on our findings that improved representational alignment with a student improves student learning outcomes (i.e., task accuracy), we design a classroom matching procedure that assigns students to teachers based on the utility curve. If we are to design effective machine teachers, it is not enough to build teachers that are accurate -- we want teachers that can align, representationally, to their students too.

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