@inproceedings{hegazy-etal-2026-guiding,
    title = "Guiding Giants: Lightweight Controllers for Weighted Activation Steering in {LLM}s",
    author = "Hegazy, Amr  and
      Elhoushi, Mostafa  and
      Alanwar, Amr",
    editor = "Chang, Kai-Wei  and
      Mehrabi, Ninareh  and
      Krishna, Satyapriya  and
      Das, Anubrata  and
      Dhamala, Jwala  and
      Cao, Yang Trista  and
      Kumarage, Tharindu  and
      Ramakrishna, Anil  and
      Christodoulopoulos, Christos  and
      Wan, Yixin  and
      Galystan, Aram  and
      Kumar, Anoop  and
      Gupta, Rahul",
    booktitle = "Proceedings of the 6th Workshop on Trustworthy {NLP} ({T}rust{NLP} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.trustnlp-main.46/",
    pages = "584--599",
    ISBN = "979-8-89176-418-7",
    abstract = "Controlling undesirable LLM behaviors typically requires costly fine-tuning, while existing inference-time steering methods lack fine-grained adaptivity. We introduce a lightweight, trainable controller network for adaptive inference-time control. The controller observes intermediate LLM activations to predict a global scaling factor and layer-specific weights, which dynamically modulate a pre-computed ``refusal direction'' vector. Trained on harmful and benign prompts, the controller learns to apply nuanced, layer-aware steering selectively. Experiments on Llama and Mistral models show our method significantly increases refusal rates on safety benchmarks like ToxicChat, outperforming existing approaches without altering the original model parameters."
}