Quality Estimation

Quality estimation (QE) focuses on automatically assessing the quality of outputs from various machine learning models, particularly in machine translation and other natural language processing tasks, without relying on human-generated reference data. Current research emphasizes leveraging large language models (LLMs) and transformer networks, often incorporating techniques like in-context learning and nearest-neighbor methods, to improve the accuracy and efficiency of QE systems. These advancements are significant because accurate QE can streamline data annotation, improve model training by filtering low-quality data, and enhance user experience by providing reliable assessments of machine-generated content.