Large Language Models as Zero-shot Labelers
Using LLMs to obtain labels for supervised models —  Introduction Labeling data is a critical step in building supervised machine learning models, as the quantity and quality of labels is often the main factor that determines model performance. However, labeling data can be very time-consuming and expensive, especially for complex tasks that involve domain knowledge or reading large amounts of…
Large Language Models as Zero-shot Labelers Using LLMs to obtain labels for supervised models —  Introduction Labeling data is a critical step in building supervised machine learning models, as the quantity and quality of labels is often the main factor that determines model performance. However, labeling data can be very time-consuming and expensive, especially for complex tasks that involve domain knowledge or reading large amounts of… nlp
Large Language Models Zeroshot Labelers Using LLMs obtain label supervised model — Introduction Labeling data critical step building supervised machine learning model quantity quality label often main factor determines model performance However labeling data timeconsuming expensive especially complex task involve domain knowledge reading large amount of… nlp