Human Generated Label

Human-generated labels are crucial for training machine learning models, particularly in complex tasks like text annotation, summarization, and action recognition, but obtaining them is expensive and time-consuming. Current research focuses on mitigating this limitation by leveraging large language models (LLMs) to generate labels, developing methods to handle noisy or incomplete LLM-generated labels through techniques like active learning and label refinement, and exploring alternative approaches such as weak supervision and preference optimization. These advancements aim to improve the efficiency and scalability of training machine learning models while addressing issues like label bias and ambiguity, ultimately impacting various fields from social science research to music analysis and video understanding.