A systematic empirical study shows concept probes need surprisingly little data for task-relevant concepts, tolerate data reuse and moderate label noise, and benefit slightly from larger probed models.
Explainable Abstract Trains Dataset
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The Explainable Abstract Trains Dataset is an image dataset containing simplified representations of trains. It aims to provide a platform for the application and research of algorithms for justification and explanation extraction. The dataset is accompanied by an ontology that conceptualizes and classifies the depicted trains based on their visual characteristics, allowing for a precise understanding of how each train was labeled. Each image in the dataset is annotated with multiple attributes describing the trains' features and with bounding boxes for the train elements.
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On the Performance of Concept Probing: The Influence of the Data (Extended Version)
A systematic empirical study shows concept probes need surprisingly little data for task-relevant concepts, tolerate data reuse and moderate label noise, and benefit slightly from larger probed models.