Kuaiji: the First Chinese Accounting Large Language Model
This work addresses the problem of domain adaptation for LLMs in accounting, specifically for Chinese users, but it is incremental as it applies existing fine-tuning methods to a new domain.
The paper tackles the challenge of adapting large language models to specialized domains like accounting by introducing Kuaiji, a tailored Chinese accounting LLM, which achieves exceptional accuracy and response speed using a dataset of genuine accountant-client dialogues.
Large Language Models (LLMs) like ChatGPT and GPT-4 have demonstrated impressive proficiency in comprehending and generating natural language. However, they encounter difficulties when tasked with adapting to specialized domains such as accounting. To address this challenge, we introduce Kuaiji, a tailored Accounting Large Language Model. Kuaiji is meticulously fine-tuned using the Baichuan framework, which encompasses continuous pre-training and supervised fine-tuning processes. Supported by CAtAcctQA, a dataset containing large genuine accountant-client dialogues, Kuaiji exhibits exceptional accuracy and response speed. Our contributions encompass the creation of the first Chinese accounting dataset, the establishment of Kuaiji as a leading open-source Chinese accounting LLM, and the validation of its efficacy through real-world accounting scenarios.