Interpreting Vision and Language Generative Models with Semantic Visual Priors

• URL: http://arxiv.org/abs/2304.14986v2
• Date: Thu, 4 May 2023 15:25:00 GMT
• Title: Interpreting Vision and Language Generative Models with Semantic Visual Priors
• Authors: Michele Cafagna, Lina M. Rojas-Barahona, Kees van Deemter, Albert Gatt
• Abstract summary: We develop a framework based on SHAP that allows for generating meaningful explanations leveraging the meaning representation of the output sequence as a whole. We demonstrate that our method generates semantically more expressive explanations than traditional methods at a lower compute cost.
• Score: 3.3772986620114374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When applied to Image-to-text models, interpretability methods often provide token-by-token explanations namely, they compute a visual explanation for each token of the generated sequence. Those explanations are expensive to compute and unable to comprehensively explain the model's output. Therefore, these models often require some sort of approximation that eventually leads to misleading explanations. We develop a framework based on SHAP, that allows for generating comprehensive, meaningful explanations leveraging the meaning representation of the output sequence as a whole. Moreover, by exploiting semantic priors in the visual backbone, we extract an arbitrary number of features that allows the efficient computation of Shapley values on large-scale models, generating at the same time highly meaningful visual explanations. We demonstrate that our method generates semantically more expressive explanations than traditional methods at a lower compute cost and that it can be generalized over other explainability methods.

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