The analysis of public affairs documents is crucial for citizens as it promotes transparency, accountability, and informed decision-making. It allows citizens to understand government policies, participate in public discourse, and hold representatives accountable. This is crucial, and sometimes a matter of life or death, for companies whose operation depend on certain regulations. Large Language Models (LLMs) have the potential to greatly enhance the analysis of public affairs documents by effectively processing and understanding the complex language used in such documents. In this work, we analyze the performance of LLMs in classifying public affairs documents. As a natural multi-label task, the classification of these documents presents important challenges. In this work, we use a regex-powered tool to collect a database of public affairs documents with more than 33K samples and 22.5M tokens. Our experiments assess the performance of 4 different Spanish LLMs to classify up to 30 different topics in the data in different configurations. The results shows that LLMs can be of great use to process domain-specific documents, such as those in the domain of public affairs.

翻译：对公共事务文档的分析对公民至关重要，因为它能促进透明度、问责制和理性决策。公民可借此了解政府政策、参与公共讨论并监督代表的责任。对于运营依赖特定法规的企业而言，这更是关乎生死存亡的关键。大语言模型（LLMs）通过有效处理和理解公共事务文档中使用的复杂语言，具有大幅增强此类文档分析能力的潜力。本研究分析了LLMs在公共事务文档分类中的性能。作为一项天然的多标签任务，此类文档的分类面临重要挑战。我们使用基于正则表达式的工具构建了一个包含33K余样本、2250万词元的公共事务文档数据库。实验评估了4种不同西班牙语LLMs在多种配置下对数据中最多30个主题进行分类的性能。结果表明，LLMs在处理公共事务等特定领域文档方面具有重要应用价值。