Analysing the Generalisation and Reliability of Steering Vectors

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. We additionally show steerability is also mostly a property of the dataset rather than the model by measuring steerability across multiple models. 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. Similarity in behaviour between distributions somewhat predicts generalisation performance, but there is more work needed to understand when and why steering vectors generalise correctly. Overall, our findings show that while steering can work well in the right circumstances, there remain many technical difficulties of applying steering vectors to robustly guide models' behaviour at scale.