Introduction
Recent advancements in large-scale language models like ChatGPT have expanded the horizons of natural language processing. These models excel at understanding complex language patterns and incorporating extensive knowledge. However, they have limitations such as struggling with rare information, generating incorrect content, and lacking access to real-time data due to fixed parameters. To address these issues, knowledge-augmented NLP methods have emerged. These methods offer a plug-and-play approach to enhance model performance by explicitly integrating external knowledge inputs. This bridges the gap between statistical learning from text patterns and comprehensive world knowledge. In essence, while large language models have made significant progress, their limitations make it crucial to integrate external knowledge. Knowledge-augmented NLP methods offer a promising solution, improving efficiency and effectiveness in language processing by empowering models to understand and utilize diverse knowledge inputs.
Workshop History
Important Dates
Paper Submission Deadline |
May 17, 2024 |
Notification of Acceptance |
June 17, 2024 |
Camera-ready deadline |
July 1, 2024 |
Workshop Date |
August 16, 2024 |