SDG target detection in environmental reports using Retrieval-augmented Generation with LLMs

AbstractWith the consolidation of Large Language Models (LLM) as a dominant component in approaches for multiple linguistic tasks, the interest in these technologies has greatly increased within a variety of areas and domains. A particular scenario of information needs where to exploit these approaches is climate-aware NLP. Paradigmatically, the vast manual labour of inspecting long, heterogeneous documents to find environment-relevant expressions and claims suits well within a recently established Retrieval-augmented Generation (RAG) framework. In this paper, we tackle two dual problems within environment analysis dealing with the common goal of detecting a Sustainable Developmental Goal (SDG) target being addressed in a textual passage of an environmental assessment report.We develop relevant test collections, and propose and evaluate a series of methods within the general RAG pipeline, in order to assess the current capabilities of LLMs for the tasks of SDG target evidence identification and SDG target detection.