A Concept-Based Explainability Framework for Large Multimodal Models

• URL: http://arxiv.org/abs/2406.08074v3
• Date: Sat, 30 Nov 2024 10:48:21 GMT
• Title: A Concept-Based Explainability Framework for Large Multimodal Models
• Authors: Jayneel Parekh, Pegah Khayatan, Mustafa Shukor, Alasdair Newson, Matthieu Cord,
• Abstract summary: We propose a dictionary learning based approach, applied to the representation of tokens. We show that these concepts are well semantically grounded in both vision and text. We show that the extracted multimodal concepts are useful to interpret representations of test samples.
• Score: 52.37626977572413
• License:
• Abstract: Large multimodal models (LMMs) combine unimodal encoders and large language models (LLMs) to perform multimodal tasks. Despite recent advancements towards the interpretability of these models, understanding internal representations of LMMs remains largely a mystery. In this paper, we present a novel framework for the interpretation of LMMs. We propose a dictionary learning based approach, applied to the representation of tokens. The elements of the learned dictionary correspond to our proposed concepts. We show that these concepts are well semantically grounded in both vision and text. Thus we refer to these as ``multi-modal concepts''. We qualitatively and quantitatively evaluate the results of the learnt concepts. We show that the extracted multimodal concepts are useful to interpret representations of test samples. Finally, we evaluate the disentanglement between different concepts and the quality of grounding concepts visually and textually. Our code is publicly available at https://github.com/mshukor/xl-vlms

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