Consistency Guided Knowledge Retrieval and Denoising in LLMs for Zero-shot Document-level Relation Triplet Extraction

Document-level Relation Triplet Extraction (DocRTE) is a fundamental task in information systems that aims to simultaneously extract entities with semantic relations from a document.Existing methods heavily rely on a substantial amount of fully labeled data.However, collecting and annotating data for newly emerging relations is time-consuming and labor-intensive.Recent advanced Large Language Models (LLMs), such as ChatGPT and LLaMA, exhibit impressive long-text generation capabilities, inspiring us to explore an alternative approach for obtaining auto-labeled documents with new relations.In this paper, we propose a Zero-shot Document-level Relation Triplet Extraction (ZeroDocRTE) framework, which Generates labeled data by Retrieval and Denoising Knowledge from LLMs, called GenRDK.Specifically, we propose a chain-of-retrieval prompt to guide ChatGPT to generate labeled long-text data step by step.To improve the quality of synthetic data, we propose a denoising strategy based on the consistency of cross-document knowledge.Leveraging our denoised synthetic data, we proceed to fine-tune the LLaMA2-13B-Chat for extracting document-level relation triplets.We perform experiments for both zero-shot document-level relation and triplet extraction on two public datasets.The experimental results illustrate that our GenRDK framework outperforms strong baselines.