Leveraging Large Language Models for Topic Classification in the Domain of Public Affairs
This work addresses the need for efficient analysis of public affairs documents to enhance transparency and accountability, though it is incremental as it applies existing LLMs to a new domain.
The study tackled the problem of classifying public affairs documents by evaluating the performance of four Spanish Large Language Models on a dataset of over 33,000 samples with 30 topics, showing that LLMs are highly useful for processing such domain-specific documents.
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.