Abstract: The knowledge extraction task aims to extract structured knowledge from complex information resources.However,existing research on knowledge extraction often relies on a large amount of manually annotated data,leading to high costs.To address this challenge,this paper proposes a zero-shot knowledge extraction method enhanced by large language models,which aims to perform knowledge extraction tasks automatically without relying on any manually annotated data,leveraging the strong semantic reasoning capabilities of large models to reduce data annotation costs.Specifically,it first preprocesses the format of the test set data and fine-tunes a general-purpose large model across domains to obtain a data annotation model.This model is then used to annotate relevant texts to extract corresponding entity and attribute inference information.Next,this paper establishes a new chain of thought prompting paradigm for this information and further fine-tunes a specialized large model for a specific domain to obtain a knowledge extraction model.Additionally,it continuously increases the data and iteratively trains to enhance the model's performance.Finally,it enhances the attribute information of the test set using the large model to improve the knowledge extraction model's understanding of the text,thereby enhancing its extraction performance.Benchmarking experiments on multiple large models further demonstrate that the proposed zero-shot knowledge extraction framework achieves a significant perfor-mance improvement.

Key words: Large language model, Zero-shot knowledge extraction, Data annotation model, Chain of thought, Knowledge extraction model