Due to the timeliness of the ChatGPT API and the fact that ChatGPT's responses to the same input are always different, it cannot be guaranteed that the results reproduced at present will match the disclosed statistical results 100%.
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This work's primary contributions are:
> Conducting a preliminary study on ChatGPT's potential in agricultural text classification and proposing ChatAgri, a ChatGPT-based solution.
> Evaluating ChatAgri on multiple multi-lingual datasets and demonstrating its competitive performance compared to existing PLM-based fine-tuning approaches, including producing a reasoning chain that simulates the human mind.
> Demonstrating ChatAgri's potential in agricultural text classification through zero-shot setting experiments, which require less supervised data, annotations, and domain expert knowledge.
> Showing ChatAgri's excellent domain transferability in multi-lingual experiments, making it adaptable to different agricultural application scenarios and accelerating the future of Artificial General Intelligence (AGI).
> Subverting the mainstream complex and power-intensive PLM-based methods, ChatAgri only relies on network interface and minimum hardware requirements, making it a promising low-cost artificial intelligence technique for future smart agricultural applications.
> Releasing the codes of ChatAgri on Github and deploying a ChatGPT-based agricultural text classification system to an online website to encourage further research on smart agricultural applications using ChatGPTs.
ChatAgri
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][ChatAgri]
Datasets
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][Datasets]
Baseline Comparisons
[
][baselinecomparisons]
ChatAgri adopted advanced constructed prompts
[
][advancedprompts]
prompt learning & ChatAgri
[
][promptingChatAgri]
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