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FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models

Year: 2026
Type: conference-paper
Abstract: Large language models (LLMs) often exhibit undesirable behaviors, such as safety violations and hallucinations.Although inference-time steering offers a cost-effective way to adjust model behavior without updating its parameters, existing methods often fail to be simultaneously effective, utilitypreserving, and training-efficient due to their rigid, one-size-fits-all designs and limited adaptability.In this work, we present FineSteer, a novel steering framework that decomposes inference-time steering into two complementary stages-conditional steering and fine-grained vector synthesis-allowing finegrained control over when and how to steer internal representations.In the first stage, we introduce a Subspace-guided Conditional Steering (SCS) mechanism that preserves model utility by avoiding unnecessary steering.In the second stage, we propose a Mixture-of-Steering-Experts (MoSE) mechanism that captures the multimodal nature of desired steering behaviors and generates query-specific steering vectors for improved effectiveness.Through tailored designs in both SCS and MoSE, FineSteer maintains robust performance on general queries while adaptively optimizing steering vectors for targeted inputs in a training-efficient manner.Extensive experiments on safety and truthfulness benchmarks show that FineSteer outperforms the state-of-the-art methods in overall performance (e.g., A 7.6% improvement on TruthfulQA over Llama-3), achieving stronger steering performance with minimal utility loss.The code is available at https://github.com/YukinoAsuna/FineSteer.
DOI: 10.18653/v1/2026.acl-long.852
Authors: Zixuan Weng, Jinghuai Zhang, Kunlin Cai, Ying Li, Peiran Wang, Yuan Tian
Language: English
Open Access status: hybrid
OpenAlex ID: W7166827879
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