Augmenting Relation Generation with Contextual Information: A Study on Large Language Models

The proposed approach offers several advantages over traditional methods. Firstly, it decomposes the complex task of contextual path generation into simpler subtasks, making it easier to solve. Secondly, it utilizes LLMs, which have been shown to be effective in various other complex tasks. Finally, it provides a balance between simplicity and thoroughness, capturing the essence of the article without oversimplifying.
In conclusion, the authors present a novel multi-step prompting strategy for contextual path generation, which improves the accuracy of KG completion tasks by utilizing LLMs to perform smaller subtasks. The proposed approach offers a more comprehensive understanding of complex relationships between entities in a context document, enabling Codex to generate more accurate paths.