@inproceedings{pan-etal-2025-survey,
    title = "A Survey on Training-free Alignment of Large Language Models",
    author = "Pan, Birong  and
      Li, Yongqi  and
      Zhang, Weiyu  and
      Lu, Wenpeng  and
      Xu, Mayi  and
      Zhou, Shen  and
      Zhu, Yuanyuan  and
      Zhong, Ming  and
      Qian, Tieyun",
    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/author-page-yu-wang-polytechnic/2025.findings-emnlp.238/",
    doi = "10.18653/v1/2025.findings-emnlp.238",
    pages = "4445--4461",
    ISBN = "979-8-89176-335-7",
    abstract = "The alignment of large language models (LLMs) aims to ensure their outputs adhere to human values, ethical standards, and legal norms. Traditional alignment methods often rely on resource-intensive fine-tuning (FT), which may suffer from knowledge degradation and face challenges in scenarios where the model accessibility or computational resources are constrained. In contrast, training-free (TF) alignment techniques{---}leveraging in-context learning, decoding-time adjustments, and post-generation corrections{---}offer a promising alternative by enabling alignment without heavily retraining LLMs, making them adaptable to both open-source and closed-source environments. This paper presents the first systematic review of TF alignment methods, categorizing them by stages of **pre-decoding**, **in-decoding**, and **post-decoding**. For each stage, we provide a detailed examination from the viewpoint of LLMs and multimodal LLMs (MLLMs), highlighting their mechanisms and limitations. Furthermore, we identify key challenges and future directions, paving the way for more inclusive and effective TF alignment techniques. By synthesizing and organizing the rapidly growing body of research, this survey offers a guidance for practitioners and advances the development of safer and more reliable LLMs."
}