Exploring the Capability of ChatGPT for Cross-Linguistic Agricultural Document Classification: Investigation and Evaluation

In the sustainable smart agriculture era, a vast amount of agricultural knowledge is available on the internet, making it necessary to explore effective document classification techniques for enhanced accessibility and efficiency. Over the past few years, fine-tuning strategies based on pre-trained language models (PLMs) have gained popularity as mainstream deep learning approaches, showcasing impressive performance. However, these approaches face several challenges, including a limited availability of training data, poor domain transferability, lack of model interpretability, and the challenges in deploying large models. Inspired by ChatGPT’s significant success, we investigate its capability and utilization in the field of agricultural information processing. We explore various attempts to maximize ChatGPT’s potential, including various prompting construction strategies, ChatGPT question-answering (Q &A) inference, and intermediate answer alignment technique. Our preliminary comparative study demonstrates that ChatGPT effectively addresses research challenges and bottlenecks, positioning it as an ideal solution for agricultural document classification. This findings encourage the development of a general-purpose agricultural document processing paradigm. Our preliminary study also indicates the trend towards achieving Artificial General Intelligence (AGI) for sustainable smart agriculture in the future. Code is available on Github ( https://github.com/albert-jin/agricultural_textual_classification_ChatGPT ).