Generation of machine-readable country-by-country reports with large language models

The object of this study is the process that generates machine-readable Country-by-Country reports in XML format using large language models. This paper addresses the task related to the current dependence of the process that generates these reports on specialized software, which leads to additional financial costs. The research and analysis of the effectiveness of publicly available large language models for generating Country-by-Country reports with new data showed high results, provided that an example model of such generation was prompted. Three large language models out of nine studied yielded results close to ideal (obtained by manual preparation or specialized systems), namely 96 points out of 100 according to the devised evaluation methodology. Four other studied models demonstrated slightly lower efficiency, but their level is also sufficient for practical use. At the same time, the resulting average cost of generating one report (US cents 4.2) is significantly lower than in the case of using specialized systems. Regarding the effectiveness of general-purpose large language models for generating Country-by-Country reports in the absence of a generation example, it is currently insufficient for practical use. In this case, all of the models studied showed results close to 0 points, i.e., completely incorrect reports were obtained. Such results are attributed to the insufficient amount of sample data during training of publicly available models. Thus, publicly available large language models could in practice replace specialized software systems designed to generate Country-by-Country reports in XML format, at least in the case of generating new reports