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.
Abstract (translated)
Climate-Eval 是一个全面的基准测试，旨在评估与气候变化相关的广泛任务中的自然语言处理模型。该基准整合了现有的数据集以及为此次发布特别开发的一套新闻分类数据集。这产生了基于13个数据集的25项任务，涵盖了气候讨论的关键方面，包括文本分类、问答和信息提取。我们的基准测试提供了一个标准化的评估工具包，用于系统地评估大型语言模型（LLMs）在这些任务上的表现。此外，我们在零样本和少样本设置中对开源LLM（从20亿参数到700亿参数）进行了广泛的评估，分析了它们在气候变化领域的优缺点。
URL
https://arxiv.org/abs/2505.18653
