Multimodal Representation Engineering for Robust AI Alignment

Keywords: multimodal AI, representation engineering, AI alignment, interpretability, safety

TL;DR: Develop a framework for representation engineering in multimodal AI systems to enhance interpretability, control, and alignment with human values across diverse input modalities.

Abstract: This research proposes to extend the concept of Representation Engineering (RepE) to multimodal AI systems, addressing the growing complexity and potential risks associated with advanced AI models that process various input types (e.g., text, images, audio). The study aims to develop techniques for analyzing and manipulating high-level representations across different modalities, enabling more precise control and interpretation of multimodal AI behaviors. We present a comprehensive framework that involves: (1) identifying and mapping cross-modal representations in large multimodal models, (2) developing methods to intervene and modify these representations to align with desired outcomes, (3) creating evaluation metrics for multimodal alignment and safety, and (4) investigating the transferability of representation engineering techniques across different multimodal architectures. Our experimental results demonstrate significant improvements in the transparency, controllability, and safety of multimodal AI systems across various benchmarks. This work has the potential to significantly contribute to the broader goal of aligning advanced AI with human values and intentions, providing a foundation for more reliable and interpretable multimodal AI systems.

Submission Number: 155