Interpretable and pedagogical examples

OpenAI Blog Papers

Summary

Research showing that iterative training of student-teacher neural networks produces interpretable teaching strategies, with the teacher learning to select or generate pedagogical examples that humans can understand and learn from effectively.

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# Interpretable and pedagogical examples Source: [https://openai.com/index/interpretable-and-pedagogical-examples/](https://openai.com/index/interpretable-and-pedagogical-examples/) ## Abstract Teachers intentionally pick the most informative examples to show their students\. However, if the teacher and student are neural networks, the examples that the teacher network learns to give, although effective at teaching the student, are typically uninterpretable\. We show that training the student and teacher iteratively, rather than jointly, can produce interpretable teaching strategies\. We evaluate interpretability by \(1\) measuring the similarity of the teacher's emergent strategies to intuitive strategies in each domain and \(2\) conducting human experiments to evaluate how effective the teacher's strategies are at teaching humans\. We show that the teacher network learns to select or generate interpretable, pedagogical examples to teach rule\-based, probabilistic, boolean, and hierarchical concepts\.

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