Sequential Explanations with Mental Model-Based Policies

By A. Yeung, Shalmali Joshi, Joseph Jay Williams, Frank Rudzicz. 2020

16 citations recorded by the source corpus.

The act of explaining across two parties is a feedback loop, where one provides information on what needs to be explained and the other provides an explanation relevant to this information. We apply a reinforcement learning framework which emulates this format by providing explanations based on the explainee's current mental model. We conduct novel online human experiments where explanations generated by various explanation methods are selected and presented to participants, using policies which observe participants' mental models, in order to optimize an interpretability proxy. Our results suggest that mental model-based policies (anchored in our proposed state representation) may increase interpretability over multiple sequential explanations, when compared to a random selection baseline. This work provides insight into how to select explanations which increase relevant information for users, and into conducting human-grounded experimentation to understand interpretability.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Measuring Association Between Labels and Free-Text Rationales (2020, background): “…has a rich history in machine learning inter-pretability as a reliable measure of rationale quality from the lens of utility to an end-user (Kim et al., 2016; Chandrasekaran et al., 2018; Hase and Bansal, 2020; Yeung et al., 2020; Poursabzi-Sangdeh et al., 2021; Rajagopal et al., 2021, i.a. ).”