OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917774522056704 |
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| author | Zhang, Ningyu Xi, Zekun Luo, Yujie Wang, Peng Tian, Bozhong Yao, Yunzhi Zhang, Jintian Deng, Shumin Sun, Mengshu Liang, Lei Zhang, Zhiqiang Zhu, Xiaowei Zhou, Jun Chen, Huajun |
| author_facet | Zhang, Ningyu Xi, Zekun Luo, Yujie Wang, Peng Tian, Bozhong Yao, Yunzhi Zhang, Jintian Deng, Shumin Sun, Mengshu Liang, Lei Zhang, Zhiqiang Zhu, Xiaowei Zhou, Jun Chen, Huajun |
| contents | Knowledge representation has been a central aim of AI since its inception. Symbolic Knowledge Graphs (KGs) and neural Large Language Models (LLMs) can both represent knowledge. KGs provide highly accurate and explicit knowledge representation, but face scalability issue; while LLMs offer expansive coverage of knowledge, but incur significant training costs and struggle with precise and reliable knowledge manipulation. To this end, we introduce OneEdit, a neural-symbolic prototype system for collaborative knowledge editing using natural language, which facilitates easy-to-use knowledge management with KG and LLM. OneEdit consists of three modules: 1) The Interpreter serves for user interaction with natural language; 2) The Controller manages editing requests from various users, leveraging the KG with rollbacks to handle knowledge conflicts and prevent toxic knowledge attacks; 3) The Editor utilizes the knowledge from the Controller to edit KG and LLM. We conduct experiments on two new datasets with KGs which demonstrate that OneEdit can achieve superior performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07497 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System Zhang, Ningyu Xi, Zekun Luo, Yujie Wang, Peng Tian, Bozhong Yao, Yunzhi Zhang, Jintian Deng, Shumin Sun, Mengshu Liang, Lei Zhang, Zhiqiang Zhu, Xiaowei Zhou, Jun Chen, Huajun Artificial Intelligence Computation and Language Databases Information Retrieval Machine Learning Knowledge representation has been a central aim of AI since its inception. Symbolic Knowledge Graphs (KGs) and neural Large Language Models (LLMs) can both represent knowledge. KGs provide highly accurate and explicit knowledge representation, but face scalability issue; while LLMs offer expansive coverage of knowledge, but incur significant training costs and struggle with precise and reliable knowledge manipulation. To this end, we introduce OneEdit, a neural-symbolic prototype system for collaborative knowledge editing using natural language, which facilitates easy-to-use knowledge management with KG and LLM. OneEdit consists of three modules: 1) The Interpreter serves for user interaction with natural language; 2) The Controller manages editing requests from various users, leveraging the KG with rollbacks to handle knowledge conflicts and prevent toxic knowledge attacks; 3) The Editor utilizes the knowledge from the Controller to edit KG and LLM. We conduct experiments on two new datasets with KGs which demonstrate that OneEdit can achieve superior performance. |
| title | OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System |
| topic | Artificial Intelligence Computation and Language Databases Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2409.07497 |