@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://aclanthology.org/2025.acl-demo.19/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T AutoAlign: Get Your LLM Aligned with Minimal Annotations
%A Lu, Xinyu
%A Xu, Dong
%A Zhang, Chunkang
%A Guan, Xinyan
%A Wang, Junxiang
%A Zhang, Qingyu
%A Wang, Pengbo
%A Mao, Yingzhi
%A Xiang, Hao
%A Wen, Xueru
%A Li, Zichao
%A Lu, Yaojie
%A Lin, Hongyu
%A Sun, Le
%A Han, Xianpei
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F lu-etal-2025-autoalign
%X 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.
%R 10.18653/v1/2025.acl-demo.19
%U https://aclanthology.org/2025.acl-demo.19/
%U https://doi.org/10.18653/v1/2025.acl-demo.19
%P 189-198
Markdown (Informal)
[AutoAlign: Get Your LLM Aligned with Minimal Annotations](https://aclanthology.org/2025.acl-demo.19/) (Lu et al., ACL 2025)
ACL
- Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, and Xianpei Han. 2025. AutoAlign: Get Your LLM Aligned with Minimal Annotations. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 189–198, Vienna, Austria. Association for Computational Linguistics.