@inproceedings{lu-etal-2025-autoalign,
    title = "{A}uto{A}lign: Get Your {LLM} Aligned with Minimal Annotations",
    author = "Lu, Xinyu  and
      Xu, Dong  and
      Zhang, Chunkang  and
      Guan, Xinyan  and
      Wang, Junxiang  and
      Zhang, Qingyu  and
      Wang, Pengbo  and
      Mao, Yingzhi  and
      Xiang, Hao  and
      Wen, Xueru  and
      Li, Zichao  and
      Lu, Yaojie  and
      Lin, Hongyu  and
      Sun, Le  and
      Han, Xianpei",
    editor = "Mishra, Pushkar  and
      Muresan, Smaranda  and
      Yu, Tao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.19/",
    pages = "189--198",
    ISBN = "979-8-89176-253-4",
    abstract = "Automated Alignment refers to a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention. However, it faces challenges such as algorithmic diversity and excessively convoluted workflows. We present AutoAlign, an open-source toolkit that offers:(1) a unified framework integrating mainstream automated algorithms through a consistent interface, and(2) an accessible workflow supporting one-click execution for prompt synthesis, automatic alignment signal construction, and iterative model training. Our toolkit enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components. It includes implementations for both highly efficient inference and training, as well as low-resource training. By standardizing automated alignment methodologies and providing accessible implementations, AutoAlign lowers the barriers to building customized aligned models and supports academic research."
}