@inproceedings{zhao-etal-2026-denoising,
    title = "Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering",
    author = "Zhao, Haiyan  and
      Wu, Xuansheng  and
      Yang, Fan  and
      Shen, Bo  and
      Liu, Ninghao  and
      Du, Mengnan",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.40/",
    pages = "797--808",
    ISBN = "979-8-89176-386-9",
    abstract = "Linear concept vectors effectively steer LLMs, but existing methods suffer from noisy features in diverse datasets that undermine steering robustness. We propose Sparse Autoencoder-Denoised Concept Vectors (SDCV), which selectively keep the most discriminative SAE latents while reconstructing hidden representations. Our key insight is that concept-relevant signals can be explicitly separated from dataset noise by scaling up activations of top-k latents that best differentiate positive and negative samples. Applied to linear probing and difference-in-mean, SDCV consistently improves steering success rates by 4-16{\%} across six challenging concepts, while maintaining topic relevance."
}