Technologies, Applications, and Challenges of Large Language Models

The rise of Large Language Models (LLMs) marks one of the most significant breakthroughs in artificial intelligence, with the potential to transform knowledge-intensive sectors. Among these, the legal field and public administration stand out as particularly suitable environments for the adoption of such technologies, given their highly normative, document-based, and procedural nature. Nevertheless, integrating LLMs into these domains raises legal, ethical, and operational issues that call for careful and systematic examination. This paper offers an overview of the key features that define LLMs and examines the main methodologies used to specialize and ground them in factual information, with particular attention to techniques such as Retrieval Augmented Generation and fine-tuning. Building on this foundation, the contribution aims to explore LLMs on two levels: first, by analyzing the legal implications associated with their use, focusing on topics such as liability, algorithmic transparency, personal data protection, bias mitigation, and the reliability of generated outputs; and second, by surveying the principal applications of LLMs in legal and administrative contexts, with the goal of providing an up to date and critically informed overview of the opportunities and challenges linked to their adoption.