ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic
  Agricultural Text Classification
Biao Zhao Weiqiang Jin Javier Del Ser Guang Yang
In the era of sustainable smart agriculture, a massive amount of agricultural
news text is being posted on the Internet, in which massive agricultural
knowledge has been accumulated. In this context, it is urgent to explore
effective text classification techniques for users to access the required
agricultural knowledge with high efficiency. Mainstream deep learning
approaches employing fine-tuning strategies on pre-trained language models
(PLMs), have demonstrated remarkable performance gains over the past few years.
Nonetheless, these methods still face many drawbacks that are complex to solve,
including: 1. Limited agricultural training data due to the expensive-cost and
labour-intensive annotation; 2. Poor domain transferability, especially of
cross-linguistic ability; 3. Complex and expensive large models
deployment.Inspired by the extraordinary success brought by the recent ChatGPT
(e.g. GPT-3.5, GPT-4), in this work, we systematically investigate and explore
the capability and utilization of ChatGPT applying to the agricultural
informatization field. ....(shown in article).... Code has been released on
Github
https://github.com/albert-jin/agricultural_textual_classification_ChatGPT.