Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change

• URL: http://arxiv.org/abs/2505.18653v1
• Date: Sat, 24 May 2025 11:45:46 GMT
• Title: Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change
• Authors: Murathan Kurfalı, Shorouq Zahra, Joakim Nivre, Gabriele Messori,
• Abstract summary: Climate-Eval aggregates existing datasets along with a newly developed news classification dataset.<n>This results in a benchmark of 25 tasks based on 13 datasets, covering key aspects of climate discourse.
• Score: 2.8680187920555635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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