Towards Structured Knowledge: Advancing Triple Extraction from Regional Trade Agreements using Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915535537569792 |
|---|---|
| author | Nandini, Durgesh Koch, Rebekka Schoenfeld, Mirco |
| author_facet | Nandini, Durgesh Koch, Rebekka Schoenfeld, Mirco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05121 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Structured Knowledge: Advancing Triple Extraction from Regional Trade Agreements using Large Language Models Nandini, Durgesh Koch, Rebekka Schoenfeld, Mirco Computation and Language Computational Engineering, Finance, and Science Information Retrieval Machine Learning 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. |
| title | Towards Structured Knowledge: Advancing Triple Extraction from Regional Trade Agreements using Large Language Models |
| topic | Computation and Language Computational Engineering, Finance, and Science Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2510.05121 |