Analyzing the Generalization and Reliability of Steering Vectors

Daniel Chee Hian Tan, David Chanin, Aengus Lynch, Adrià Garriga-Alonso, Dimitrios Kanoulas, Brooks Paige, Robert Kirk

Keywords: Mechanistic Interpretability, Representation Engineering, Steering Vectors, Alignment, Generalization, Causal Abstraction

TL;DR: We evaluate steering vectors in- and out-of-distribution for 40 datasets and find that they are susceptible to spurious correlations.

Abstract: Steering vectors (SVs) are a new approach to efficiently adjust language model behaviour at inference time by intervening on intermediate model activations. They have shown promise in terms of improving both capabilities and model alignment. However, the reliability and generalisation properties of this approach are unknown. In this work, we rigorously investigate these properties, and show that steering vectors have substantial limitations both in- and out-of-distribution. In-distribution, steerability is highly variable across different inputs. Depending on the concept, spurious biases can substantially contribute to how effective steering is for each input, presenting a challenge for the widespread use of steering vectors. Out-of-distribution, while steering vectors often generalise well, for several concepts they are brittle to reasonable changes in the prompt, resulting in them failing to generalise well. Overall, our findings show that while steering can work well in the right circumstances, there remain many technical difficulties of applying steering vectors to guide models' behaviour at scale.

Submission Number: 67