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--- |
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title: README |
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emoji: 🏆 |
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colorFrom: green |
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colorTo: pink |
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sdk: static |
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pinned: false |
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--- |
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We are dedicated to LLMs that serve the agricultural sector. Specifically, due to the current lack of fine-tuning datasets for LLMs in crop science, |
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we have released our CROP dataset, which is a large open-source dataset with over 210K Q&A pairs. Furthermore, to provide a high-quality evaluation standard for this vertical domain, |
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we have introduced the CROP benchmark, which is a large open-source dataset with 5045 multiple-choice questions. |
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We hope our work will advance the field of LLMs in agricultural production and contribute to solving hunger issues. |
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## Note |
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**Our work is accepted by NeurIPS2024 Dataset & Benchmark Track.** |
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All datasets and benchmarks are open-sourced. You can see our project website at **https://github.com/RenqiChen/The_Crop** for more details about our work. |
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## BibTeX & Citation |
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If you find our codes and datasets useful, please consider citing our work: |
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```bibtex |
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@inproceedings{zhangempowering, |
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title={Empowering and Assessing the Utility of Large Language Models in Crop Science}, |
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author={Zhang, Hang and Sun, Jiawei and Chen, Renqi and Liu, Wei and Yuan, Zhonghang and Zheng, Xinzhe and Wang, Zhefan and Yang, Zhiyuan and Yan, Hang and Zhong, Han-Sen and others}, |
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booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track} |
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} |
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``` |
