Towards Structured Knowledge: Advancing Triple Extraction from Regional Trade Agreements using Large Language Models
This work addresses the problem of creating economic trade knowledge graphs from legal texts for economists and policymakers, but it appears incremental as it applies existing LLM methods to a new domain without major methodological breakthroughs.
The study tackled extracting structured knowledge as triples from regional trade agreement texts using Large Language Models, achieving results through zero-shot, one-shot, and few-shot prompting techniques with the Llama 3.1 model, though no concrete performance numbers were provided.
This study investigates the effectiveness of Large Language Models (LLMs) for the extraction of structured knowledge in the form of Subject-Predicate-Object triples. We apply the setup for the domain of Economics application. The findings can be applied to a wide range of scenarios, including the creation of economic trade knowledge graphs from natural language legal trade agreement texts. As a use case, we apply the model to regional trade agreement texts to extract trade-related information triples. In particular, we explore the zero-shot, one-shot and few-shot prompting techniques, incorporating positive and negative examples, and evaluate their performance based on quantitative and qualitative metrics. Specifically, we used Llama 3.1 model to process the unstructured regional trade agreement texts and extract triples. We discuss key insights, challenges, and potential future directions, emphasizing the significance of language models in economic applications.