@inproceedings{xu-etal-2025-easyedit2,
title = "{E}asy{E}dit2: An Easy-to-use Steering Framework for Editing Large Language Models",
author = "Xu, Ziwen and
Wang, Shuxun and
Xu, Kewei and
Xu, Haoming and
Wang, Mengru and
Deng, Xinle and
Yao, Yunzhi and
Zheng, Guozhou and
Chen, Huajun and
Zhang, Ningyu",
editor = {Habernal, Ivan and
Schulam, Peter and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-demos.38/",
pages = "522--535",
ISBN = "979-8-89176-334-0",
abstract = "In this paper, we introduce EasyEdit2, a framework designed to enable plug-and-play adjustability for controlling Large Language Model (LLM) behaviors. EasyEdit2 supports a wide range of test-time interventions, including safety, sentiment, personality, reasoning patterns, factuality, and language features. Unlike its predecessor, EasyEdit2 features a new architecture specifically designed for seamless model steering. It comprises key modules such as the steering vector generator and the steering vector applier, which enable automatic generation and application of steering vectors to influence the model{'}s behavior without modifying its parameters. One of the main advantages of EasyEdit2 is its ease of use{---}users do not need extensive technical knowledge. With just a single example, they can effectively guide and adjust the model{'}s responses, making precise control both accessible and efficient. Empirically, we report model steering performance across different LLMs, demonstrating the effectiveness of these techniques. We have released the source code on https://github.com/zjunlp/EasyEdit along with a demonstration notebook. In addition, we provide an online system at http://easyedit.zjukg.cn/for real-time model steering, and a demo video at https://www.youtube.com/watch?v=AkfoiPfp5rQ."
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<abstract>In this paper, we introduce EasyEdit2, a framework designed to enable plug-and-play adjustability for controlling Large Language Model (LLM) behaviors. EasyEdit2 supports a wide range of test-time interventions, including safety, sentiment, personality, reasoning patterns, factuality, and language features. Unlike its predecessor, EasyEdit2 features a new architecture specifically designed for seamless model steering. It comprises key modules such as the steering vector generator and the steering vector applier, which enable automatic generation and application of steering vectors to influence the model’s behavior without modifying its parameters. One of the main advantages of EasyEdit2 is its ease of use—users do not need extensive technical knowledge. With just a single example, they can effectively guide and adjust the model’s responses, making precise control both accessible and efficient. Empirically, we report model steering performance across different LLMs, demonstrating the effectiveness of these techniques. We have released the source code on https://github.com/zjunlp/EasyEdit along with a demonstration notebook. In addition, we provide an online system at http://easyedit.zjukg.cn/for real-time model steering, and a demo video at https://www.youtube.com/watch?v=AkfoiPfp5rQ.</abstract>
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%0 Conference Proceedings
%T EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models
%A Xu, Ziwen
%A Wang, Shuxun
%A Xu, Kewei
%A Xu, Haoming
%A Wang, Mengru
%A Deng, Xinle
%A Yao, Yunzhi
%A Zheng, Guozhou
%A Chen, Huajun
%A Zhang, Ningyu
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F xu-etal-2025-easyedit2
%X In this paper, we introduce EasyEdit2, a framework designed to enable plug-and-play adjustability for controlling Large Language Model (LLM) behaviors. EasyEdit2 supports a wide range of test-time interventions, including safety, sentiment, personality, reasoning patterns, factuality, and language features. Unlike its predecessor, EasyEdit2 features a new architecture specifically designed for seamless model steering. It comprises key modules such as the steering vector generator and the steering vector applier, which enable automatic generation and application of steering vectors to influence the model’s behavior without modifying its parameters. One of the main advantages of EasyEdit2 is its ease of use—users do not need extensive technical knowledge. With just a single example, they can effectively guide and adjust the model’s responses, making precise control both accessible and efficient. Empirically, we report model steering performance across different LLMs, demonstrating the effectiveness of these techniques. We have released the source code on https://github.com/zjunlp/EasyEdit along with a demonstration notebook. In addition, we provide an online system at http://easyedit.zjukg.cn/for real-time model steering, and a demo video at https://www.youtube.com/watch?v=AkfoiPfp5rQ.
%U https://aclanthology.org/2025.emnlp-demos.38/
%P 522-535
Markdown (Informal)
[EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models](https://aclanthology.org/2025.emnlp-demos.38/) (Xu et al., EMNLP 2025)
ACL
- Ziwen Xu, Shuxun Wang, Kewei Xu, Haoming Xu, Mengru Wang, Xinle Deng, Yunzhi Yao, Guozhou Zheng, Huajun Chen, and Ningyu Zhang. 2025. EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 522–535, Suzhou, China. Association for Computational Linguistics.