Graphical Reasoning: LLM-based Semi-Open Relation Extraction

arXiv:2405.00216v1 Announce Type: cross
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 …

abstract advanced arxiv chain of thought context cs.ai cs.cl cs.lg example exploration extraction gpt gpt-3 gpt-3.5 in-context learning language language models llm novel paper process reasoning thought through type