Interpretable and Pedagogical Examples
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.
 This is a well written paper on a compelling topic: how to train "an automated teacher" to use intuitive strategies that would also apply to humans. The introduction is fairly strong, but this reviewer wishes that the authors would have come up with an intuitive example that illustrates why the strategy "1) train S on random exs; 2) train T to pick exs for S" makes sense. Such an example would dramatically improve the paper*s readability. The paper appears to be original, and the related work section is quite extensive. A second significant improvement would be to add an in-depth running example in section 3, so that the authors could illustrate why the BR strategy makes sense (Algorithm 2).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Interpretable and Pedagogical Examples
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This is a well written paper on a compelling topic: how to train "an automated teacher" to use intuitive strategies that would also apply to humans. The introduction is fairly strong, but this reviewer wishes that the authors would have come up with an intuitive example that illustrates why the strategy "1) train S on random exs; 2) train T to pick exs for S" makes sense. Such an example would dramatically improve the paper*s readability. The paper appears to be original, and the related work section is quite extensive. A second significant improvement would be to add an in-depth running example in section 3, so that the authors could illustrate why the BR strategy makes sense (Algorithm 2).
assistant
 This is a well written paper on a compelling topic: how to train "an automated teacher" to use intuitive strategies that would also apply to humans. The introduction is fairly strong, but this reviewer wishes that the authors would have come up with an intuitive example that illustrates why the strategy "1) train S on random exs; 2) train T to pick exs for S" makes sense. Such an example would dramatically improve the paper*s readability. The paper appears to be original, and the related work section is quite extensive. A second significant improvement would be to add an in-depth running example in section 3, so that the authors could illustrate why the BR strategy makes sense (Algorithm 2).