用ChatGPT生成数据，提升食品风险与产品检测模型表现
Data Augmentation to Improve Large Language Models in Food Hazard and Product Detection
- 用ChatGPT-4o-mini生成增强数据，扩充原始训练集
- 在召回率、F1值等指标上均优于原数据训练的模型
- 适合关注食品安全文本分类的NLP研究者
本研究旨在验证使用ChatGPT-4o-mini进行数据增强对食品危害与产品分析的影响。通过ChatGPT-4o-mini生成的增强数据被用于训练RoBERTa-base和Flan-T5-base两个大语言模型，并在测试集上评估性能。结果表明，相较于仅使用原始数据，采用增强数据可显著提升模型在召回率、F1分数、精确率和准确率等关键指标上的表现。完整代码及训练流程、增强数据集均可在该仓库获取：https://github.com/AREEG94FAHAD/food-hazard-prdouct-cls。
原文摘要 · Abstract (English)
The primary objective of this study is to demonstrate the impact of data augmentation using ChatGPT-4o-mini on food hazard and product analysis. The augmented data is generated using ChatGPT-4o-mini and subsequently used to train two large language models: RoBERTa-base and Flan-T5-base. The models are evaluated on test sets. The results indicate that using augmented data helped improve model performance across key metrics, including recall, F1 score, precision, and accuracy, compared to using only the provided dataset. The full code, including model training and the augmented dataset, can be found in this repository: https://github.com/AREEG94FAHAD/food-hazard-prdouct-cls
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