Testing Pretrained Large Language Models to Set Up a Knowledge Hub of Heterogeneous Multisource Environmental Documents

This contribution outlines the design of a Knowledge Hub of heterogeneous documents related to the UNEP/MAP Barcelona Convention system. The Knowledge Hub is intended to serve as a resource to assist public authorities and users with different backgrounds and needs in accessing information efficiently; users should be able to either formulate natural language queries or to navigate a knowledge graph that is automatically generated to find relevant documents. The ad hoc retrieval task and the Knowledge Hub creation are defined based on state-of-the-art Large Language Models (LLMs). Specifically, this contribution focuses on a user-evaluation experiment that tested publicly available pretrained foundation Large Language Models (LLMs) for retrieving a subset of documents with varying lengths and topics.