Abstract: With the emergence of ChatGPT,large language models have begun to play a significant role across various industries,from general fields to specialized domains.Although there have been methods combining artificial intelligence with auditing,the application of large language models in auditing still needs further research due to the fact that the accuracy of traditional artificial intelligence methods is much lower than that of existing large language models.The use of AI methods to intelligently identify useful entities within text in auditing can greatly enhance work efficiency and reduce errors.Conventional auditing text entity recog-nition algorithms primarily rely on machine learning combined with feature engineering,which generally results in lower accuracy.In light of this,this study investigates the applications of several common open-source models(such as Llama) and closed-source models(such as ChatGPT) in auditing text entity recognition,while integrating contextual learning techniques to improve model recognition performance.The results demonstrate that by employing a sample organization method based on similarity selection,the accuracy of entity recognition can be improved to 98.3%,achieving notable improvements.

Key words: Audit, Large language models, ChatGPT, Named entity recognition, In-context learning