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DIAL: Steerable Language Models with Distilled Instruction-Adherence Levels

Thanh Q. Tran, John Russell Himawan, Arun Verma, Kiwan Wong, Wei Xiao, Daniela Rus, Bryan Kian Hsiang Low · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31438 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

Stepting 使用者表示“保持指示短”,LLM 的响应应该有多短? 更一般地说,用户和应用程序需要不同水平的指示遵守,但Prompting 不能可靠地提供对它们的细粒控制。因此,我们研究“可控制的语言模型”,这些模型提供细粒数控的程度,当LLM遵循文本中规定的通用指示时。然而,现有的方法不共同提供细粒控制,效率和可扩展性在各种指令中。 为了解决这一差距,我们引入了DIAL(分布式指令附属级别)几乎所有三种属性。 对于细粒控制,我们执行分类自由指南(CFG)与冷冻的LLM,以便在不同级别的指令附属性概念上提供培训信号。

Machine translation (Meta M2M100); the English original follows.

Abstract

When a user says "Keep it short", how short should the LLM's response be? More generally, users and applications require different levels of instruction adherence, but prompting does not reliably provide fine-grained control over them. We therefore study "steerable language models" that provide fine-grained numerical control over the degree to which models follow general instructions specified in text. However, existing approaches do not jointly offer fine-grained control, efficiency, and scalability across diverse instructions. To address this gap, we introduce DIAL (Distilled Instruction-Adherence Levels) to achieve all three properties. For fine-grained control, we perform classifier-free guidance (CFG) with a frozen LLM to provide training signals for on-policy distillation of instruction adherence at different levels. For efficiency, we design a lightweight steering adapter that uses a classifier over the LLM's hidden states to compute a closed-form steering edit. The classifier adaptively sets the steering magnitude at each token based on the target adherence level, while accounting for the geometry of the LLM's hidden states to preserve response coherence. For scalability, the classifier is text-conditioned, allowing a shared adapter to support new instructions without per-instruction fitting, and training requires only instruction and request text. Evaluation across multiple LLM families shows that DIAL achieves a competitive balance between concept expression and response fluency, with concept scores increasing monotonically across adherence levels. We introduce SteerIF, an instruction-only dataset of 98, 625 concept, persona, verifiable, and descriptive instructions. When trained on SteerIF, DIAL provides fine-grained control over personas and, when combined with prompting, improves verifiable instruction following.

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Topics: Computer science, Classifier (UML), Security token, Language model, Artificial intelligence, Scalability

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