CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification

arXiv:2403.11904v1 Announce Type: cross
Abstract: 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, …

abstract and natural language processing arxiv class classification context cs.cl cs.lg dataset food health human in-context learning language language processing machine machine learning natural natural language natural language processing processing public recall risk risks training type web