Why Representation Engineering Works: A Theoretical and Empirical Study in Vision-Language Models
This work addresses AI robustness, fairness, and transparency issues in multimodal AI systems, representing an incremental extension of existing methods to a new domain.
The paper tackles the problem of vision-language models (VLMs) producing hallucinated responses where visual input overrides factual linguistic knowledge, by extending Representation Engineering (RepE) to VLMs and developing a theoretical framework explaining its stability through principal eigenvectors. The result demonstrates broad applicability and significance of these intrinsic properties, transforming RepE into a structured theoretical framework.
Representation Engineering (RepE) has emerged as a powerful paradigm for enhancing AI transparency by focusing on high-level representations rather than individual neurons or circuits. It has proven effective in improving interpretability and control, showing that representations can emerge, propagate, and shape final model outputs in large language models (LLMs). However, in Vision-Language Models (VLMs), visual input can override factual linguistic knowledge, leading to hallucinated responses that contradict reality. To address this challenge, we make the first attempt to extend RepE to VLMs, analyzing how multimodal representations are preserved and transformed. Building on our findings and drawing inspiration from successful RepE applications, we develop a theoretical framework that explains the stability of neural activity across layers using the principal eigenvector, uncovering the underlying mechanism of RepE. We empirically validate these instrinsic properties, demonstrating their broad applicability and significance. By bridging theoretical insights with empirical validation, this work transforms RepE from a descriptive tool into a structured theoretical framework, opening new directions for improving AI robustness, fairness, and transparency.