EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866916298240294912 |
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| author | Wang, Peng Zhang, Ningyu Tian, Bozhong Xi, Zekun Yao, Yunzhi Xu, Ziwen Wang, Mengru Mao, Shengyu Wang, Xiaohan Cheng, Siyuan Liu, Kangwei Ni, Yuansheng Zheng, Guozhou Chen, Huajun |
| author_facet | Wang, Peng Zhang, Ningyu Tian, Bozhong Xi, Zekun Yao, Yunzhi Xu, Ziwen Wang, Mengru Mao, Shengyu Wang, Xiaohan Cheng, Siyuan Liu, Kangwei Ni, Yuansheng Zheng, Guozhou Chen, Huajun |
| contents | Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. To this end, many knowledge editing approaches for LLMs have emerged -- aiming to subtly inject/edit updated knowledge or adjust undesired behavior while minimizing the impact on unrelated inputs. Nevertheless, due to significant differences among various knowledge editing methods and the variations in task setups, there is no standard implementation framework available for the community, which hinders practitioners from applying knowledge editing to applications. To address these issues, we propose EasyEdit, an easy-to-use knowledge editing framework for LLMs. It supports various cutting-edge knowledge editing approaches and can be readily applied to many well-known LLMs such as T5, GPT-J, LlaMA, etc. Empirically, we report the knowledge editing results on LlaMA-2 with EasyEdit, demonstrating that knowledge editing surpasses traditional fine-tuning in terms of reliability and generalization. We have released the source code on GitHub, along with Google Colab tutorials and comprehensive documentation for beginners to get started. Besides, we present an online system for real-time knowledge editing, and a demo video. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_07269 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models Wang, Peng Zhang, Ningyu Tian, Bozhong Xi, Zekun Yao, Yunzhi Xu, Ziwen Wang, Mengru Mao, Shengyu Wang, Xiaohan Cheng, Siyuan Liu, Kangwei Ni, Yuansheng Zheng, Guozhou Chen, Huajun Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Information Retrieval Machine Learning Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. To this end, many knowledge editing approaches for LLMs have emerged -- aiming to subtly inject/edit updated knowledge or adjust undesired behavior while minimizing the impact on unrelated inputs. Nevertheless, due to significant differences among various knowledge editing methods and the variations in task setups, there is no standard implementation framework available for the community, which hinders practitioners from applying knowledge editing to applications. To address these issues, we propose EasyEdit, an easy-to-use knowledge editing framework for LLMs. It supports various cutting-edge knowledge editing approaches and can be readily applied to many well-known LLMs such as T5, GPT-J, LlaMA, etc. Empirically, we report the knowledge editing results on LlaMA-2 with EasyEdit, demonstrating that knowledge editing surpasses traditional fine-tuning in terms of reliability and generalization. We have released the source code on GitHub, along with Google Colab tutorials and comprehensive documentation for beginners to get started. Besides, we present an online system for real-time knowledge editing, and a demo video. |
| title | EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2308.07269 |