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

Date Issued
2023-10
Author(s)
Lazarev, Gorgi
Chitkushev, Ljubomir
Vodenska, Irena
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.
File(s)
Loading...
Thumbnail Image
Name

comparing_the_performance_of_chatgpt_and_state_of_art_climate_nlp_models_on_climate_related_text_classification_tasks.pdf

Size

1.45 MB

Format

Adobe PDF

Checksum

(MD5):3123b0c05bc64e41e7bdcf21f25d67a6