“Intelligent auditing represents a crucial advancement in modern audit practices, enhancing boththe quality and efficiency of audits within the realm of artificial intelligence. With the rise oflarge language model (LLM), there is enormous potential for intelligent models to contribute toaudit domain. However, general LLMs applied in audit domain face the challenges of lackingspecialized knowledge and the presence of data biases. To overcome these challenges, this studyintroduces AuditWen, an open-source audit LLM by fine-tuning Qwen with constructing instruc-tion data from audit domain. We first outline the application scenarios for LLMs in the audit andextract requirements that shape the development of LLMs tailored for audit purposes. We thenpropose an audit LLM, called AuditWen, by fine-tuning Qwen with constructing 30k instructiondataset from 15 audit tasks and 3 layers. In evaluation stage, we proposed a benchmark with 5kinstructions that covers a set of critical audit tasks derived from the application scenarios. Withthe benchmark, we compare AuditWen with other existing LLMs from information extraction,question answering and document generation. The experimental results demonstrate superiorperformance of AuditWen both in question understanding and answer generation, making it animmediately valuable tool for audit.Keyword AuditWen, LLM, instruction dataset, fine-tuning, benchmarkIntroduction”