MULTIVIZ: TOWARDS VISUALIZING AND UNDERSTANDING MULTIMODAL MODELS

Abstract

The promise of multimodal models for real-world applications has inspired research in visualizing and understanding their internal mechanics with the end goal of empowering stakeholders to visualize model behavior, perform model debugging, and promote trust in machine learning models. However, modern multimodal models are typically black-box neural networks, which makes it challenging to understand their internal mechanics. How can we visualize the internal modeling of multimodal interactions in these models? Our paper aims to fill this gap by proposing MULTIVIZ, a method for analyzing the behavior of multimodal models by scaffolding the problem of interpretability into 4 stages: (1) unimodal importance: how each modality contributes towards downstream modeling and prediction, (2) cross-modal interactions: how different modalities relate with each other, (3) multimodal representations: how unimodal and cross-modal interactions are represented in decision-level features, and (4) multimodal prediction: how decision-level features are composed to make a prediction. MULTIVIZ is designed to operate on diverse modalities, models, tasks, and research areas. Through experiments on 8 trained models across 6 real-world tasks, we show that the complementary stages in MULTIVIZ together enable users to (1) simulate model predictions, (2) assign interpretable concepts to features, (3) perform error analysis on model misclassifications, and (4) use insights from error analysis to debug models. MULTIVIZ is publicly available, will be regularly updated with new interpretation tools and metrics, and welcomes inputs from the community.

1. INTRODUCTION

The recent promise of multimodal models that integrate information from heterogeneous sources of data has led to their proliferation in numerous real-world settings such as multimedia (Naphade et al., 2006 ), affective computing (Poria et al., 2017 ), robotics (Lee et al., 2019 ), and healthcare (Xu et al., 2019) . Subsequently, their impact towards real-world applications has inspired recent research in visualizing and understanding their internal mechanics (Liang et al., 2022; Goyal et al., 2016; Park et al., 2018) as a step towards accurately benchmarking their limitations for more reliable deployment (Hendricks et al., 2018; Jabri et al., 2016) . However, modern parameterizations of multimodal models are typically black-box neural networks, such as pretrained transformers (Li et al., 2019; Lu et al., 2019) . How can we visualize and understand the internal modeling of multimodal information and interactions in these models? As a step in interpreting multimodal models, this paper introduces an analysis and visualization method called MULTIVIZ (see Figure 1 ). To tackle the challenges of visualizing model behavior, we scaffold the problem of interpretability into 4 stages: (1) unimodal importance: identifying the contributions of each modality towards downstream modeling and prediction, (2) cross-modal interactions: uncovering the various ways in which different modalities can relate with each other and the types of new information possibly discovered as a result of these relationships, (3) multimodal representations: how unimodal and cross-modal interactions are represented in decision-level features, and (4) multimodal prediction: how decision-level features are composed to make a prediction for a given task. In addition to including current approaches for unimodal importance (Goyal et al., 2016; Merrick and Taly, 2020; Ribeiro et al., 2016) and cross-modal interactions (Hessel and Lee, 2020; Lyu et al., 2022) , we additionally propose new methods for interpreting cross-modal interactions, multimodal representations, and prediction to complete these stages in MULTIVIZ. By viewing multimodal interpretability through the lens of these 4 stages, MULTIVIZ contributes a modular and human-in-the-loop visualization toolkit for the community to visualize popular multimodal

