EntGPT: Linking Generative Large Language Models with Knowledge Bases
Yifan Ding Amrit Poudel Qingkai Zeng Tim Weninger Balaji Veeramani Sanmitra Bhattacharya
The ability of Large Language Models (LLMs) to generate factually correct
output remains relatively unexplored due to the lack of fact-checking and
knowledge grounding during training and inference. In this work, we aim to
address this challenge through the Entity Disambiguation (ED) task. We first
consider prompt engineering, and design a three-step hard-prompting method to
probe LLMs' ED performance without supervised fine-tuning (SFT). Overall, the
prompting method improves the micro-F_1 score of the original vanilla models by
a large margin, on some cases up to 36% and higher, and obtains comparable
performance across 10 datasets when compared to existing methods with SFT. We
further improve the knowledge grounding ability through instruction tuning (IT)
with similar prompts and responses. The instruction-tuned model not only
achieves higher micro-F1 score performance as compared to several baseline
methods on supervised entity disambiguation tasks with an average micro-F_1
improvement of 2.1% over the existing baseline models, but also obtains higher
accuracy on six Question Answering (QA) tasks in the zero-shot setting. Our
methodologies apply to both open- and closed-source LLMs.