LLIMONIIE: Large Language Instructed Model for Open Named Italian Information Extraction

Abstract The exponential growth of unstructured documents generated daily underscores the urgent need to develop technologies to structure information effectively. Traditional Information Extraction (IE) models permit transforming textual data to structured formats (e.g., semantic triplets), enabling efficient searches and uncovering hidden data insights. However, they require predefined ontologies and, often, extensive human efforts. On the other hand, Open IE tools extract information without any input knowledge, but they are limited in capturing entire and in-depth contexts. Furthermore, the state of the art presents a substantial discrepancy between the efforts carried out in English-centric methods and other low-resource languages. Our study addresses the aforementioned key challenges: the need for a high-performative human effort agnostic model and overcoming the lack of Italian IE resources. To this end, we introduce Open Named Information Extraction (ONIE), a novel approach to generalize IE across diverse domains without requiring input ontologies and capturing complex relationships. In this work, we propose LLIMONIIE, a novel end-to-end framework that leverages the capabilities of Large Language Models (LLMs) to perform ONIE from entire documents, able to uniformly extract Named Entities and Open Relations. Furthermore, we devised an innovative dataset generation methodology to support our research on Italian IE. The code and dataset are publicly available, contributing to the scientific community and development of low-resource languages. Experiments demonstrate the potential of LLIMONIIE, achieving competitive results compared to the actual Italian information extraction state of the art.