Contaminated or adulterated food poses a substantial risk to human health.
Given sets of labeled web texts for training, Machine Learning and Natural
Language Processing can be applied to automatically detect such risks. We
publish a dataset of 7,546 short texts describing public food recall
announcements. Each text is manually labeled, on two granularity levels (coarse
and fine), for food products and hazards that the recall corresponds to. We
describe the dataset and benchmark naive, traditional, and Transformer models.
Based on our analysis, Logistic Regression based on a tf-idf representation
outperforms RoBERTa and XLM-R on classes with low support. Finally, we discuss
different prompting strategies and present an LLM-in-the-loop framework, based
on Conformal Prediction, which boosts the performance of the base classifier
while reducing energy consumption compared to normal prompting.

污染或掺假食品对人类健康构成重大风险，我们提供了一个包含 7,546 个短文本的数据集，描述公共食品召回通知，通过机器学习和自然语言处理自动检测食品风险，并基于数据集进行了基于逻辑回归的 tf-idf 表示的模型性能比较，最终提出了一种基于 Conformal Prediction 的 LLM-in-the-loop 框架，提高基础分类器的性能，并降低了能源消耗。