KRPO Framework Enhances Open-Domain Relational Triplet Extraction with LLMs

A novel approach called Knowledge Restoration-driven Prompt Optimization (KRPO) has been introduced to enhance Open-domain Relational Triplet Extraction (ORTE) utilizing Large Language Models (LLMs). ORTE seeks to extract structured knowledge without relying on predefined relation schemas, and LLMs have shifted it towards a prompt-driven approach via in-context learning. Nonetheless, adapting LLM extraction to diverse open-domain scenarios poses challenges. Current techniques depend on static, manually designed prompts, which fail to accommodate the wide range of linguistic variations and contextual frameworks, resulting in unsupported triplets and inconsistencies. The lack of ground-truth annotations complicates the identification and rectification of these issues. Moreover, free-form relation generation leads to non-standard relation forms, jeopardizing knowledge graph integrity. KRPO tackles these challenges by offering a label-free target-corpus adaptation framework. This research aims to bolster the reliability and consistency of knowledge extraction in open-domain contexts, with the paper accessible on arXiv under identifier 2601.15037v2, categorized as replace-cross.