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incorrect facts owing to the outdated/noisy data.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 to apply 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 apply 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
at this https URL, 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 at
this http URL

大型语言模型（LLMs）通常存在知识截断或谬误问题，为解决此问题，我们提出了 EasyEdit，一个易于使用的 LLMs 知识编辑框架，它支持各种前沿的知识编辑方法，并在可靠性和推广性方面超过了传统微调。