@inproceedings{ghosh-etal-2025-simple,
title = "A Simple Yet Effective Method for Non-Refusing Context Relevant Fine-grained Safety Steering in {LLM}s",
author = "Ghosh, Shaona and
Bhattacharjee, Amrita and
Ziser, Yftah and
Parisien, Christopher",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1781/",
pages = "35116--35136",
ISBN = "979-8-89176-332-6",
abstract = "Fine-tuning large language models (LLMs) to meet evolving safety policies is costly and impractical. Mechanistic interpretability enables inference-time control through latent activation steering, but its potential for precise, customizable safety adjustments remains underexplored. We propose SafeSteer, a simple and effective method to guide LLM outputs by (i) leveraging category-specific steering vectors for fine-grained control, (ii) applying a gradient-free, unsupervised approach that enhances safety while preserving text quality and topic relevance without forcing explicit refusals, and (iii) eliminating the need for contrastive safe data. Across multiple LLMs, datasets, and risk categories, SafeSteer provides precise control, avoids blanket refusals, and directs models to generate safe, relevant content, aligning with recent findings that simple activation-steering techniques often outperform more complex alternatives."
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<abstract>Fine-tuning large language models (LLMs) to meet evolving safety policies is costly and impractical. Mechanistic interpretability enables inference-time control through latent activation steering, but its potential for precise, customizable safety adjustments remains underexplored. We propose SafeSteer, a simple and effective method to guide LLM outputs by (i) leveraging category-specific steering vectors for fine-grained control, (ii) applying a gradient-free, unsupervised approach that enhances safety while preserving text quality and topic relevance without forcing explicit refusals, and (iii) eliminating the need for contrastive safe data. Across multiple LLMs, datasets, and risk categories, SafeSteer provides precise control, avoids blanket refusals, and directs models to generate safe, relevant content, aligning with recent findings that simple activation-steering techniques often outperform more complex alternatives.</abstract>
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%0 Conference Proceedings
%T A Simple Yet Effective Method for Non-Refusing Context Relevant Fine-grained Safety Steering in LLMs
%A Ghosh, Shaona
%A Bhattacharjee, Amrita
%A Ziser, Yftah
%A Parisien, Christopher
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F ghosh-etal-2025-simple
%X Fine-tuning large language models (LLMs) to meet evolving safety policies is costly and impractical. Mechanistic interpretability enables inference-time control through latent activation steering, but its potential for precise, customizable safety adjustments remains underexplored. We propose SafeSteer, a simple and effective method to guide LLM outputs by (i) leveraging category-specific steering vectors for fine-grained control, (ii) applying a gradient-free, unsupervised approach that enhances safety while preserving text quality and topic relevance without forcing explicit refusals, and (iii) eliminating the need for contrastive safe data. Across multiple LLMs, datasets, and risk categories, SafeSteer provides precise control, avoids blanket refusals, and directs models to generate safe, relevant content, aligning with recent findings that simple activation-steering techniques often outperform more complex alternatives.
%U https://aclanthology.org/2025.emnlp-main.1781/
%P 35116-35136
Markdown (Informal)
[A Simple Yet Effective Method for Non-Refusing Context Relevant Fine-grained Safety Steering in LLMs](https://aclanthology.org/2025.emnlp-main.1781/) (Ghosh et al., EMNLP 2025)
ACL