OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
Journal:
arXiv
Published Date:
Sep 9, 2024
Abstract
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.
