3 accepted papers
Many sensitive domains — such as the clinical domain — lack widely available datasets due to privacy risks. The increasing generative capabilities of large language models (LLMs) have made synthetic datasets a viable path forward. In this study, we domain-adapt LLMs to the clinical domain and genera…
Food safety demands timely detection, regulation, and public communication, yet the lack of structured datasets hinders Natural Language Processing (NLP) research. We present and release a new dataset of human-written and Large Language Model (LLM)-generated summaries of food safety documents, plus
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 announc…