Abstract
Generative artificial intelligence, particularly large language models (LLMs), offers significant potential for improving access to legal information and supporting decision-making. However, integrating these technologies into legal information systems introduces challenges related to reliability, explainability, jurisdictional constraints, and the temporal validity of legal knowledge. This paper examines these challenges and how they shape the architecture and functionality of legal information systems. To address them, we introduce JurisGraph, a conceptual model that captures structured legal knowledge. By combining structured representations of legal concepts and relationships with LLMs and retrieval-augmented generation, supported by multi-agent orchestration, such systems can improve the transparency, consistency, and trustworthiness of their outputs. The paper highlights the importance of combining unstructured generative capabilities with structured domain knowledge in the design of reliable AI-driven legal systems.
Paper Type
Poster
DOI
10.62036/ISD.2026.63
Enabling Reliable Legal Information Systems with LLMs and generative AI: A Structured Approach
Generative artificial intelligence, particularly large language models (LLMs), offers significant potential for improving access to legal information and supporting decision-making. However, integrating these technologies into legal information systems introduces challenges related to reliability, explainability, jurisdictional constraints, and the temporal validity of legal knowledge. This paper examines these challenges and how they shape the architecture and functionality of legal information systems. To address them, we introduce JurisGraph, a conceptual model that captures structured legal knowledge. By combining structured representations of legal concepts and relationships with LLMs and retrieval-augmented generation, supported by multi-agent orchestration, such systems can improve the transparency, consistency, and trustworthiness of their outputs. The paper highlights the importance of combining unstructured generative capabilities with structured domain knowledge in the design of reliable AI-driven legal systems.
Recommended Citation
Marković, G., Beliga, S. & Mestrovic, A.(2026). Enabling Reliable Legal Information Systems with LLMs and generative AI: A Structured Approach. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.63