Large Language Models in Legislative Content Analysis: A Dataset from the Polish Parliament
This work addresses the need for domain-specific NLP tools in the legal field, particularly for Polish language applications, but it is incremental as it applies existing LLM methods to new data.
The researchers tackled the problem of analyzing legislative content in the Polish legal system by introducing a novel dataset from official sources and evaluating large language models (LLMs) on three NLP tasks, finding that LLMs can automate and enhance analysis while facing challenges like legal context understanding.
Large language models (LLMs) are among the best methods for processing natural language, partly due to their versatility. At the same time, domain-specific LLMs are more practical in real-life applications. This work introduces a novel natural language dataset created by acquired data from official legislative authorities' websites. The study focuses on formulating three natural language processing (NLP) tasks to evaluate the effectiveness of LLMs on legislative content analysis within the context of the Polish legal system. Key findings highlight the potential of LLMs in automating and enhancing legislative content analysis while emphasizing specific challenges, such as understanding legal context. The research contributes to the advancement of NLP in the legal field, particularly in the Polish language. It has been demonstrated that even commonly accessible data can be practically utilized for legislative content analysis.