Intelligent Management and Artificial Intelligence: Trends, Challenges, and Opportunities, Vol.2

Purpose: The study analyses the potential of Generative Artificial Intelligence (GenAI) in the field of tax law by assessing the ability of Large Language Models (LLMs) to analyse legal texts. The aim is to determine whether LLMs can perform correct and practically useful analyses of tax law. Need for the study: The increasing complexity of legal regulations and the demand for effective legal research require the implementation of AI tools to support lawyers. Tax law, due to partial harmonisation in European Union countries, is an ideal case for evaluating the effectiveness of LLMs in legal analysis. Methodology: The study used an experimental method to test three LLMs: GPT o1-preview (OpenAI), Gemini 2.0 Flash Thinking Experimental 01-21 (GoogleAI Studio) and DeepSeek. The models analysed 54 rulings of Polish administrative courts. Three hypotheses were verified regarding the ability of LLMs to: (1) correctly identify legal provisions and rulings, (2) recognise key legislation and legal institutions, and (3) extract legal arguments from rulings. The results were compared with expert assessments. Findings: The study showed that LLMs can effectively support lawyers in the analysis of tax law. The models achieved high precision in the identification of legal provisions and rulings, with the Gemini 2.0 model achieving the best results. In the more complex analysis of legal theses, the GPT o1-preview model showed the best performance. Overall, the use of LLMs reduced the time needed for legal analysis by up to 90% compared to the work of experts. Practical Implications: The study emphasises the potential of LLMs in increasing the efficiency of tax law analysis and legal cases research. Lawyers can use these models to automate document review, improve legal argumentation and reduce costs. Future research should focus on integrating LLMs into the preparation of pleadings, court justifications and administrative decisions.