Graphical Reasoning: LLM-based Semi-Open Relation Extraction
It addresses relation extraction for natural language processing, with incremental advancements in method design.
The paper tackles relation extraction by using Chain of Thought and Graphical Reasoning with GPT-3.5, showing considerable performance improvements on multiple datasets.
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.