Graphical Reasoning: LLM-based Semi-Open Relation Extraction

• URL: http://arxiv.org/abs/2405.00216v1
• Date: Tue, 30 Apr 2024 21:41:53 GMT
• Title: Graphical Reasoning: LLM-based Semi-Open Relation Extraction
• Authors: Yicheng Tao, Yiqun Wang, Longju Bai,
• Abstract summary: We show how leveraging in-context learning with GPT-3.5 can significantly enhance the extraction process. We introduce a novel graphical reasoning approach that dissects relation extraction into sequential sub-tasks.
• Score: 3.2586315449885106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a comprehensive exploration of relation extraction utilizing advanced language models, specifically Chain of Thought (CoT) and Graphical Reasoning (GRE) techniques. We demonstrate how leveraging in-context learning with GPT-3.5 can significantly enhance the extraction process, particularly through detailed example-based reasoning. Additionally, we introduce a novel graphical reasoning approach that dissects relation extraction into sequential sub-tasks, improving precision and adaptability in processing complex relational data. Our experiments, conducted on multiple datasets, including manually annotated data, show considerable improvements in performance metrics, underscoring the effectiveness of our methodologies.

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