[LR] Representation Engineering: Top-Down AI Transparency
[This review is intended solely for my personal learning] Paper Info arXiv: 2310.01405v4 Title: Representation Engineering: A Top-Down Approach to AI Transparency Authors: Evan Hubinger, Ajeya Cotra, Buck Shlegeris, Tom Lieberum, Nicholas Joseph, Owain Evans, Nicholas Schiefer, Oliver Zhang, Jan Brauner, Collin Burns, Leo Gao, Ryan Greenblatt Prior Knowledge Interpretability in AI: Traditional approaches often focus on low-level features such as neurons or circuits. However, such bottom-up analysis struggles with high-level abstractions like honesty or power-seeking. Representation Learning: Neural networks form internal embeddings of concepts during training, enabling powerful generalization and emergent behavior. Mechanistic Interpretability: Aims to reverse-engineer model internals to understand reasoning processes, but often lacks tools for directly modifying internal representations to improve safety. Transparency and Alignment: Transparency is critical for ensuring model behavior is aligned with human intentions and values, particularly to avoid deceptive alignment and inner misalignment. Goal The paper proposes and develops Representation Engineering (RepE), a new top-down framework for interpretability and model control. Rather than focusing on neurons or circuits, RepE centers on representations of high-level cognitive concepts and aims to understand, detect, and manipulate them. The core goal is to advance transparency methods that are directly useful for improving AI safety, including controlling undesirable behaviors like deception or power-seeking. ...