Comparing the performance of ChatGPT and state-of-the-art climate NLP models on climate-related text classification tasks

Abstract

Recently, there has been a surge in general-purpose language models, with ChatGPT being the most advanced model to date. These models are primarily used for generating text in response to user prompts on various topics. It needs to be validated how accurate and relevant the generated text from ChatGPT is on the specific topics, as it is designed for general conversation and not for context-specific purposes. This study explores how ChatGPT, as a general-purpose model, performs in the context of a real-world challenge such as climate change compared to ClimateBert, a state-of-the-art language model specifically trained on climaterelated data from various sources, including texts, news, and papers. ClimateBert is fine-tuned on five different NLP classification tasks, making it a valuable benchmark for comparison with the ChatGPT on various NLP tasks. The main results show that for climate-specific NLP tasks, ClimateBert outperforms ChatGPT.