Summary of Oneedit: a Neural-symbolic Collaboratively Knowledge Editing System, by Ningyu Zhang et al.
OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
by Ningyu Zhang, Zekun Xi, Yujie Luo, Peng Wang, Bozhong Tian, Yunzhi Yao, Jintian Zhang, Shumin Deng, Mengshu Sun, Lei Liang, Zhiqiang Zhang, Xiaowei Zhu, Jun Zhou, Huajun Chen
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Databases (cs.DB); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel AI system, called OneEdit, is introduced for collaborative knowledge editing. This system combines symbolic Knowledge Graphs (KGs) and neural Large Language Models (LLMs) to facilitate accurate and efficient knowledge management. The OneEdit prototype consists of three modules: an Interpreter that handles user interaction with natural language, a Controller that manages editing requests and resolves conflicts using KG rollbacks, and an Editor that edits both KG and LLM based on the controlled input. Experimental results on two new datasets demonstrate the superior performance of OneEdit. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OneEdit is a new way to work together on information. It helps people edit knowledge in a safe and organized way by combining two powerful tools: Knowledge Graphs (KGs) and Large Language Models (LLMs). This system has three parts: one that talks with users, one that decides what to do, and one that makes changes. It’s tested on new datasets and shows it can do better. |
