A Fine-Tuning Approach for T5 Using Knowledge Graphs to Address Complex Tasks

Understand

With the development of deep learning technology, large language models have achieved remarkable results in many natural language processing tasks.

• However, these models still have certain limitations in handling complex reasoning tasks and understanding rich background knowledge.
• To solve this problem, this study proposed a T5 model fine-tuning method based on knowledge graphs, which enhances the model's reasoning ability and context understanding ability by introducing external knowledge graphs.
• We used the SQuAD1.1 dataset for experiments.