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

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1965))

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Abstract

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-**/agricultural_textual_classification_ChatGPT).

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Notes

  1. 1.

    PromptPerfect service: https://promptperfect.**aai.cn/prompts.

  2. 2.

    https://github.com/albert-**/agricultural_textual_classification_ChatGPT.

  3. 3.

    PromptPerfect service is available at https://promptperfect.**aai.cn/prompts.

  4. 4.

    Spacy can be accessed on https://spacy.io.

  5. 5.

    PestObserver-France download: https://github.com/sufianj/fast-camembert.

  6. 6.

    https://github.com/albert-**/agricultural-_textual_classification_ChatGPT.

  7. 7.

    More details can be accessed from http://zjzx.cnki.net/.

  8. 8.

    https://github.com/Dong-UTIL/Natural-Hazards-Twitter-Dataset.

  9. 9.

    GloVe embedding: https://nlp.stanford.edu/projects/glove/.

  10. 10.

    Huggingface transformers: https://huggingface.co/docs/transformers/index.

  11. 11.

    Baidu Ernie Bot: https://wenxin.baidu.com/wenxin/nlp.

  12. 12.

    https://huggingface.co/docs/transformers/v4.29.1/en/index.

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Acknowledgements

This work was carried out during the first author’s (Wei-qiang **) and the second author’s (Biao Zhao) research time at **‘an Jiaotong University. Weiqiang ** is the corresponding author. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence this work. We would like to thank Guizhong Liu for providing helpful discussions and recommendations. Thanks are also due to the anonymous reviewers and action editors for improving the paper with their comments, and recommendations.

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Authors and Affiliations

  1. School of Information and Communications Engineering, **’an Jiaotong University, Innovation Harbour, **’an, Shaanxi, China

    Weiqiang **, Biao Zhao & Guizhong Liu

Authors
  1. Weiqiang **
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  2. Biao Zhao
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  3. Guizhong Liu
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Corresponding author

Correspondence to Weiqiang ** .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Bei**g, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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**, W., Zhao, B., Liu, G. (2024). Exploring the Capability of ChatGPT for Cross-Linguistic Agricultural Document Classification: Investigation and Evaluation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_18

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  • DOI: https://doi.org/10.1007/978-981-99-8145-8_18

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