ClimateEval unifies 25 climate-related NLP tasks into one benchmark and shows that open-source LLMs gain from few-shot examples but lag on misinformation and fine-grained entity recognition.
Environmental Claim Detection
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abstract
To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable. To analyze such claims at scale, automated methods are needed to detect them in the first place. However, there exist no datasets or models for this. Thus, this paper introduces the task of environmental claim detection. To accompany the task, we release an expert-annotated dataset and models trained on this dataset. We preview one potential application of such models: We detect environmental claims made in quarterly earning calls and find that the number of environmental claims has steadily increased since the Paris Agreement in 2015.
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Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change
ClimateEval unifies 25 climate-related NLP tasks into one benchmark and shows that open-source LLMs gain from few-shot examples but lag on misinformation and fine-grained entity recognition.