Compositional Steering of Large Language Models with Steering Tokens

2026-01-08

arXiv.org

AI Summary

Existing approaches struggle to achieve compositional control over multiple behaviors in large language models. This work proposes Compositional Steering Tokens, a method that distills natural language instructions into dedicated input tokens and trains composable behavior tokens within the input token space, enabling zero-shot compositional control of multiple behaviors for the first time. The approach generalizes to unseen combinations and quantities of behaviors, overcoming the limitations of conventional activation-based steering. Experimental results demonstrate that the method significantly outperforms baseline techniques—including instruction prompting, activation steering, and LoRA fusion—across multiple mainstream large language model architectures. Furthermore, combining the proposed tokens with natural language instructions yields additional improvements in controllability.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Behavior Learning & Control

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Deploying LLMs in real-world applications requires controllable output that satisfies multiple desiderata at the same time. While existing work extensively addresses LLM steering for a single behavior, \textit{compositional steering} -- i.e., steering LLMs simultaneously towards multiple behaviors -- remains an underexplored problem. In this work, we propose \emph{compositional steering tokens} for multi-behavior steering. We first embed individual behaviors, expressed as natural language instructions, into dedicated tokens via self-distillation. Contrary to most prior work, which operates in the activation space, our behavior steers live in the space of input tokens, enabling more effective zero-shot composition. We then train a dedicated \textit{composition token} on pairs of behaviors and show that it successfully captures the notion of composition: it generalizes well to \textit{unseen} compositions, including those with unseen behaviors as well as those with an unseen \textit{number} of behaviors. Our experiments across different LLM architectures show that steering tokens lead to superior multi-behavior control compared to competing approaches (instructions, activation steering, and LoRA merging). Moreover, we show that steering tokens complement natural language instructions, with their combination resulting in further gains.

Problem

• compositional steering
• large language models
• multi-behavior control
• steering tokens

Innovation

• compositional steering
• steering tokens
• self-distillation
• zero-shot composition
• multi-behavior control