Analyzing the Generalization and Reliability of Steering Vectors

2024-07-17

arXiv.org

AI Summary

This study systematically evaluates the generalizability and robustness of activation-based linear interventions—i.e., steering vectors—for behavioral control in language models, addressing critical reliability bottlenecks hindering their scalable deployment. We introduce a controlled prompt suite, induce distributional shifts, perform bias attribution analysis, and conduct cross-task evaluation to enable the first multi-dimensional empirical assessment of steering vectors. Results reveal substantial in-distribution performance volatility, susceptibility to spurious correlations and prompt perturbations, and fragile out-of-distribution generalization—particularly failing on core concepts with pronounced inter-concept heterogeneity. These findings expose the intrinsic fragility of steering vectors, challenging the implicit assumption that they serve as universal, reliable intervention tools. The work provides both a cautionary insight and empirical grounding for developing trustworthy, controllable language model editing methods. Code is publicly available.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Steering vectors (SVs) have been proposed as an effective approach to 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 technical difficulties of applying steering vectors to guide models' behaviour at scale. Our code is available at https://github.com/dtch1997/steering-bench

Problem

• Vector Representation
• Language Model
• Scalability

Innovation

• Steering Vectors
• Language Model Adaptation
• Bias Sensitivity