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Towards Automatic Concept-based Explanations
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Interpretability has become an important topic of research as more machine learning (ML) models are deployed and widely used to make important decisions. Most of the current explanation methods provide explanations through feature importance scores, which identify features that are important for each individual input. However, how to systematically summarize and interpret such per sample feature importance scores itself is challenging. In this work, we propose principles and desiderata for \emph{concept} based explanation, which goes beyond per-sample features to identify higher-level human-understandable concepts that apply across the entire dataset. We develop a new algorithm, ACE, to automatically extract visual concepts. Our systematic experiments demonstrate that \alg discovers concepts that are human-meaningful, coherent and important for the neural network's predictions.
Forward citations
Cited by 4 Pith papers
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Reliable concept presence in transformers is concentrated in the extreme high-activation tail of in-concept tokens; thresholding that tail improves concept detection and localization.
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A Concept-based approach to Voice Disorder Detection
Concept bottleneck and concept embedding models, trained on clinical concepts extracted from patient notes by a large language model, detect voice pathology from audio almost as accurately as an end-to-end transformer.
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Cross-Layer Discrete Concept Discovery for Interpreting Language Models
CLVQ-VAE maps lower-layer transformer activations to higher-layer ones through a discrete codebook, yielding concept vectors evaluated with probe ablation and human annotation.
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