CORAL: Collaborative Automatic Labeling System Based on Large Language Models

Abstract

In the era of big data, data annotation is integral to numerous applications. However, it is widely acknowledged as a laborious and time-consuming process, significantly impeding the scalability and efficiency of data-driven applications. To reduce the human cost, we demonstrate CORAL, a collaborative automatic labeling system driven by large language models (LLMs), which achieves high-quality annotation with the least human effort. Firstly, CORAL employs LLM to automatically annotate vast datasets, generating coarse-grained labels. Subsequently, a weakly-supervised learning module trains small language models (SLMs) using noisy label learning techniques to distill accurate labels from LLM's annotations. It also allows statistical analysis of model outcomes to identify potentially erroneous labels, reducing the human cost of error detection. Furthermore, CORAL supports iterative refinement by LLMs and SLMs using manually corrected labels, thereby ensuring continual enhancement in annotation quality and model performance. A visual interface enables annotation process monitoring and result analysis.