The Importance of Human-Labeled Data in the Era of LLMs
Yang Liu
The advent of large language models (LLMs) has brought about a revolution in
the development of tailored machine learning models and sparked debates on
redefining data requirements. The automation facilitated by the training and
implementation of LLMs has led to discussions and aspirations that human-level
labeling interventions may no longer hold the same level of importance as in
the era of supervised learning. This paper presents compelling arguments
supporting the ongoing relevance of human-labeled data in the era of LLMs.