Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change
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Abstract (arXiv)
Climate-Eval is a comprehensive benchmark designed to evaluate natural language processing models across a broad range of tasks related to climate change. Climate-Eval 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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 24 May 2025.
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9 of 25 references.
References · 9
- 2019Quantifying the Carbon Emissions of Machine Learningcited by 521
- 2018A Survey on Deep Learning for Named Entity Recognitioncited by 282
- 2020CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claimscited by 196
- 2023The Semantic Scholar Open Data Platformcited by 118
- 2020ClimaText: A Dataset for Climate Change Topic Detectioncited by 22
- 2023ClimateBERT-NetZero: Detecting and Assessing Net Zero and Reduction Targetscited by 17
- 2022A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweetscited by 11
- 2023Towards Answering Climate Questionnaires from Unstructured Climate Reportscited by 10
- 2024INDUS: Effective and Efficient Language Models for Scientific Applicationscited by 6
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Citing papers per year: 2025: 1.
1 paper.