Representational Alignment Supports Effective Machine Teaching
<|reference_start|>Representational Alignment Supports Effective Machine Teaching: 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.<|reference_end|>
@article{sucholutsky2024representational,
    title={Representational Alignment Supports Effective Machine Teaching},
    author={Ilia Sucholutsky and Katherine M. Collins and Maya Malaviya and Nori
  Jacoby and Weiyang Liu and Theodore R. Sumers and Michalis Korakakis and
  Umang Bhatt and Mark Ho and Joshua B. Tenenbaum and Brad Love and Zachary A.
  Pardos and Adrian Weller and Thomas L. Griffiths},
    journal={arXiv preprint arXiv:2406.04302},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04302},
    primaryClass={cs.LG}
}