@inproceedings{yeo-etal-2025-understanding,
    title = "Understanding Refusal in Language Models with Sparse Autoencoders",
    author = "Yeo, Wei Jie  and
      Prakash, Nirmalendu  and
      Neo, Clement  and
      Satapathy, Ranjan  and
      Lee, Roy Ka-Wei  and
      Cambria, Erik",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.338/",
    doi = "10.18653/v1/2025.findings-emnlp.338",
    pages = "6377--6399",
    ISBN = "979-8-89176-335-7",
    abstract = "Refusal is a key safety behavior in aligned language models, yet the internal mechanisms driving refusals remain opaque. In this work, we conduct a mechanistic study of refusal in instruction-tuned LLMs using sparse autoencoders to identify latent features that causally mediate refusal behaviors. We apply our method to two open-source chat models and intervene on refusal-related features to assess their influence on generation, validating their behavioral impact across multiple harmful datasets. This enables a fine-grained inspection of how refusal manifests at the activation level and addresses key research questions such as investigating upstream-downstream latent relationship and understanding the mechanisms of adversarial jailbreaking techniques. We also establish the usefulness of refusal features in enhancing generalization for linear probes to out-of-distribution adversarial samples in classification tasks."
}