Abstract
With 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.- Anthology ID:
- 2024.climatenlp-1.19
- Volume:
- Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dominik Stammbach, Jingwei Ni, Tobias Schimanski, Kalyan Dutia, Alok Singh, Julia Bingler, Christophe Christiaen, Neetu Kushwaha, Veruska Muccione, Saeid A. Vaghefi, Markus Leippold
- Venues:
- ClimateNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 241–250
- Language:
- URL:
- https://aclanthology.org/2024.climatenlp-1.19
- DOI:
- Cite (ACL):
- Dario Garigliotti. 2024. SDG target detection in environmental reports using Retrieval-augmented Generation with LLMs. In Proceedings of the 1st Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2024), pages 241–250, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- SDG target detection in environmental reports using Retrieval-augmented Generation with LLMs (Garigliotti, ClimateNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.climatenlp-1.19.pdf