@inproceedings{kurfali-etal-2025-climateeval,
    title = "{C}limate{E}val: A Comprehensive Benchmark for {NLP} Tasks Related to Climate Change",
    author = "Kurfali, Murathan  and
      Zahra, Shorouq  and
      Nivre, Joakim  and
      Messori, Gabriele",
    editor = "Dutia, Kalyan  and
      Henderson, Peter  and
      Leippold, Markus  and
      Manning, Christoper  and
      Morio, Gaku  and
      Muccione, Veruska  and
      Ni, Jingwei  and
      Schimanski, Tobias  and
      Stammbach, Dominik  and
      Singh, Alok  and
      Su, Alba (Ruiran)  and
      A. Vaghefi, Saeid",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)",
    month = jul,
    year = "2025",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.climatenlp-1.13/",
    pages = "194--207",
    ISBN = "979-8-89176-259-6",
    abstract = "ClimateEval is a comprehensive benchmark designed to evaluate natural language processing models across a broad range of tasks related to climate change. ClimateEval aggregates existing datasets along with a newly developed news classification dataset, created specifically for this release. This results in a benchmark of 25 tasks based on 13 datasets, covering key aspects of climate discourse, including text classification, question answering, and information extraction. Our benchmark provides a standardized evaluation suite for systematically assessing the performance of large language models (LLMs) on these tasks. Additionally, we conduct an extensive evaluation of open-source LLMs (ranging from 2B to 70B parameters) in both zero-shot and few-shot settings, analyzing their strengths and limitations in the domain of climate change."
}