EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909785152028672 |
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| author | Xu, Ziwen Wang, Shuxun Xu, Kewei Xu, Haoming Wang, Mengru Deng, Xinle Yao, Yunzhi Zheng, Guozhou Chen, Huajun Zhang, Ningyu |
| author_facet | Xu, Ziwen Wang, Shuxun Xu, Kewei Xu, Haoming Wang, Mengru Deng, Xinle Yao, Yunzhi Zheng, Guozhou Chen, Huajun Zhang, Ningyu |
| contents | 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 GitHub at https://github.com/zjunlp/EasyEdit along with a demonstration notebook. In addition, we provide a demo video at https://www.youtube.com/watch?v=AkfoiPfp5rQ for a quick introduction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15133 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models Xu, Ziwen Wang, Shuxun Xu, Kewei Xu, Haoming Wang, Mengru Deng, Xinle Yao, Yunzhi Zheng, Guozhou Chen, Huajun Zhang, Ningyu Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning 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 GitHub at https://github.com/zjunlp/EasyEdit along with a demonstration notebook. In addition, we provide a demo video at https://www.youtube.com/watch?v=AkfoiPfp5rQ for a quick introduction. |
| title | EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2504.15133 |