Activation steering improves multi-value compliance in large language models
VIAS: Value-Isolating Activation Steering for Multi-Value Alignment and Compliance for Organisations
Computation and LanguageArtificial IntelligenceMachine LearningMultiagent Systems
Summary
Organizations want AI chatbots to follow multiple rules or values at the same time. The authors check if a technique called activation steering, which changes the AI’s thinking process directly, helps it follow these values better than just giving instructions (prompting). They found that creating steering signals for each value separately is tricky, so they made a new method called VIAS that isolates each value’s effect. This method improved the AI’s ability to follow all relevant rules in workplace scenarios for several different models.
What this means in practice
- •For enterprise ai teams: Enhance AI agents’ adherence to multiple organizational policies simultaneously using VIAS activation steering combined with prompting.
- •For chatbot developers: Improve multi-value compliance in chatbots by isolating and steering distinct value signals without retraining model weights.
Authors
Avni Mittal, Avinash Anand, Dikshant Kukreja, Erik Cambria, Rajiv Ratn Shah, Sandipan Dandapat
Abstract
Organisations increasingly deploy large language model agents that must follow organisational values. We check whether activation steering, which adds vectors to a model's internal activations during generation without changing its weights, can improve compliance beyond prompting alone. Since most scenarios involve several values at once, we focus on multi-value steering. A simple way to build these vectors is to contrast responses that follow all values relevant to a scenario with responses that violate them. However, this gives nearly identical vectors for different values: they capture a general compliance signal rather than individual values, which limits their use when combining several values. We propose Value-Isolating Activation Steering (VIAS), which contrasts responses to the same scenario under two prompts, one listing all relevant values and one omitting a single target value. We keep a pair only if the target value changes from compliant to non-compliant and both responses still meet a minimum compliance level on the other values. We evaluate VIAS on PROTECT, a workplace compliance dataset where each scenario is tagged with relevant values from a 21-value taxonomy. VIAS reduces overlap between value vectors, making them better suited for multi-value steering, although this alone does not lead to independent control over each value. Combined with prompting, VIAS complies with every tagged value on 69.6% of scenarios for Qwen2.5-7B, 75.2% for Mistral-7B, and 72.6% for Phi-3.5-mini, outperforming prompting alone for all three models.