Mohammad Reza Taesiri


2024

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Fine-tuning Language Models for Triple Extraction with Data Augmentation
Yujia Zhang | Tyler Sadler | Mohammad Reza Taesiri | Wenjie Xu | Marek Reformat
Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)

Advanced language models with impressive capabilities to process textual information can more effectively extract high-quality triples, which are the building blocks of knowledge graphs. Our work examines language models’ abilities to extract entities and the relationships between them. We use a diverse data augmentation process to fine-tune large language models to extract triples from the text. Fine-tuning is performed using a mix of trainers from HuggingFace and five public datasets, such as different variations of the WebNLG, SKE, DocRed, FewRel, and KELM. Evaluation involves comparing model outputs with test-set triples based on several criteria, such as type, partial, exact, and strict accuracy.The obtained results outperform ChatGPT and even match or exceed the performance of GPT-4.