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

TL;DR

This paper introduces a fine-tuning method for the T5 language model that incorporates knowledge graphs to improve reasoning and understanding in complex NLP tasks, demonstrating significant performance gains.

Contribution

It presents a novel approach to enhance T5's reasoning and context understanding by integrating external knowledge graphs, especially for complex problem-solving.

Findings

Knowledge graph scale positively impacts model performance.

Entity and relationship embeddings are crucial for effectiveness.

Knowledge graphs significantly improve reasoning in complex tasks.

Abstract

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. The experimental results show that the T5 model based on knowledge graphs is significantly better than other baseline models in reasoning accuracy, context understanding, and the ability to handle complex problems. At the same time, we also explored the impact of knowledge graphs of different scales on model performance and found that as the scale of the knowledge graph increases, the performance of the model gradually improves. Especially when dealing with complex problems, the introduction of knowledge graphs greatly improves the reasoning ability of the T5 model. Ablation experiments further verify the importance of entity and relationship embedding in the model and prove that a complete knowledge graph is crucial to improving the various capabilities of the T5 model. In summary, this study provides an effective method to enhance the reasoning and understanding capabilities of large language models and provides new directions for future research.