Annotation Error

Annotation error, the presence of inaccuracies in manually labeled datasets, significantly impacts the performance and reliability of machine learning models, particularly in natural language processing and computer vision. Current research focuses on developing methods to detect and correct these errors, employing techniques ranging from simple consistency checks and statistical modeling of annotator behavior to leveraging the capabilities of large language models (LLMs) for automated error detection and correction. Addressing annotation error is crucial for improving the trustworthiness and generalizability of machine learning models across various applications, leading to more robust and reliable systems.