This is the official implementation of the paper "MM-SHAP: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models & Tasks" accepted at ACL 2023 Toronto https://aclanthology.org/2023.acl-long.223/ .
@inproceedings{parcalabescu-frank-2023-mm,
title = "{MM}-{SHAP}: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models {\&} Tasks",
author = "Parcalabescu, Letitia and
Frank, Anette",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.223",
doi = "10.18653/v1/2023.acl-long.223",
pages = "4032--4059",
abstract = "Vision and language models (VL) are known to exploit unrobust indicators in individual modalities (e.g., introduced by distributional biases) instead of focusing on relevant information in each modality. That a unimodal model achieves similar accuracy on a VL task to a multimodal one, indicates that so-called unimodal collapse occurred. However, accuracy-based tests fail to detect e.g., when the model prediction is wrong, while the model used relevant information from a modality.Instead, we propose MM-SHAP, a performance-agnostic multimodality score based on Shapley values that reliably quantifies in which proportions a multimodal model uses individual modalities. We apply MM-SHAP in two ways: (1) to compare models for their average degree of multimodality, and (2) to measure for individual models the contribution of individual modalities for different tasks and datasets.Experiments with six VL models {--} LXMERT, CLIP and four ALBEF variants {--} on four VL tasks highlight that unimodal collapse can occur to different degrees and in different directions, contradicting the wide-spread assumption that unimodal collapse is one-sided. Based on our results, we recommend MM-SHAP for analysing multimodal tasks, to diagnose and guide progress towards multimodal integration. Code available at https://github.com/Heidelberg-NLP/MM-SHAP.",
}
To run experiments with CLIP, LXMERT and ALBEF models, run the corresponding script mm-shap_[MODEL]_dataset.py. You need to download the data from their corresponding repositories, for example:
- VALSE 💃: https://github.com/Heidelberg-NLP/VALSE
- VQA: https://visualqa.org/download.html
- GQA: https://cs.stanford.edu/people/dorarad/gqa/download.html
The Shapley value implementation in the shap folder is a modified version of https://github.com/slundberg/shap .