@inproceedings{wang-etal-2025-token,
    title = "Token-Aware Editing of Internal Activations for Large Language Model Alignment",
    author = "Wang, Tianbo  and
      Ma, Yuqing  and
      Liao, Kewei  and
      Yang, Chengzhao  and
      Zhang, Zhange  and
      Wang, Jiakai  and
      Liu, Xianglong",
    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://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.480/",
    pages = "9482--9520",
    ISBN = "979-8-89176-332-6",
    abstract = "Intervening the internal activations of large language models (LLMs) provides an effective inference-time alignment approach to mitigate undesirable behaviors, such as generating erroneous or harmful content, thereby ensuring safe and reliable applications of LLMs. However, previous methods neglect the misalignment discrepancy among varied tokens, resulting in deviant alignment direction and inflexible editing strength. To address these issues, we propose a token-aware editing (TAE) approach to fully utilize token-level alignment information in the activation space, therefore realizing superior post-intervention performance. Specifically, a Mutual Information-guided Graph Aggregation (MIG) module first develops an MI-guided graph to exploit the tokens' informative interaction for activation enrichment, thus improving alignment probing and facilitating intervention. Subsequently, Misalignment-aware Adaptive Intervention (MAI) comprehensively perceives the token-level misalignment degree from token representation and prediction to guide the adaptive adjustment of editing strength, thereby enhancing final alignment performance. Extensive experiments on three alignment capabilities demonstrate the efficacy of TAE, notably surpassing baseline by 25.8{\%} on the primary metric of truthfulness with minimal cost."
}