Intelligent Assisted Decision-Making Framework for Domain-Specific Advice Using Large- Language Models

Large language models have demonstrated remarkable capabilities in facilitating general human-computer interactions. However, their effectiveness in providing domain-specific information in professional fields is limited. In this paper, we present an intelligent assisted decision-making framework utilizing large-language models to provide professional advice in taxation. By augmenting the large language model with a domain-specific database, we aim to overcome the limitations of generic language models and provide professional advice for tax-related queries. The results of our study indicate that the proposed framework sur-passes the credibility of generic large language models by providing domain-specific advice in taxation.