What Do VLMs NOTICE? A Mechanistic Interpretability Pipeline for Noise-free Text-Image Corruption and Evaluation

2024-06-24

arXiv.org

AI Summary

This work addresses the opacity of visual-language models’ (VLMs’) decision-making processes, which hinders their deployment in high-stakes applications. To this end, we propose NOTICE—the first noise-free, joint vision-language causal attribution framework. NOTICE integrates semantic-minimal-pair (SMP) image perturbations with symmetric token replacement (STR), enabling coordinated, semantics-preserving interventions across modalities. Coupled with causal mediation analysis and cross-attention head attribution, it systematically identifies critical intermediate-layer attention heads in mainstream VLMs (e.g., BLIP). Experiments demonstrate that these heads exhibit both cross-task generalizability and functional specialization—such as implicit image segmentation and object suppression—thereby significantly enhancing decision interpretability and transparency on benchmark datasets including SVO-Probes, MIT-States, and facial expression recognition tasks.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Vision-Language Models (VLMs) have gained community-spanning prominence due to their ability to integrate visual and textual inputs to perform complex tasks. Despite their success, the internal decision-making processes of these models remain opaque, posing challenges in high-stakes applications. To address this, we introduce NOTICE, the first Noise-free Text-Image Corruption and Evaluation pipeline for mechanistic interpretability in VLMs. NOTICE incorporates a Semantic Minimal Pairs (SMP) framework for image corruption and Symmetric Token Replacement (STR) for text. This approach enables semantically meaningful causal mediation analysis for both modalities, providing a robust method for analyzing multimodal integration within models like BLIP. Our experiments on the SVO-Probes, MIT-States, and Facial Expression Recognition datasets reveal crucial insights into VLM decision-making, identifying the significant role of middle-layer cross-attention heads. Further, we uncover a set of ``universal cross-attention heads'' that consistently contribute across tasks and modalities, each performing distinct functions such as implicit image segmentation, object inhibition, and outlier inhibition. This work paves the way for more transparent and interpretable multimodal systems.

Problem

• Mechanistic interpretability in Vision-Language Models
• Noise-free text-image corruption and evaluation
• Universal cross-attention heads in VLMs

Innovation

• Noise-free Text-Image Corruption
• Semantic Minimal Pairs framework
• Symmetric Token Replacement