Individual Annotator

Individual annotator research focuses on understanding and improving the quality and consistency of human-provided labels in machine learning, particularly for natural language processing tasks. Current research explores methods to identify reliable annotators, mitigate biases introduced by individual annotators (including those reflected in LLMs used as annotators), and model annotator variability to enhance model accuracy and fairness. This work is crucial for building robust and reliable AI systems, as the quality of training data directly impacts model performance and reduces the reliance on expensive and time-consuming manual annotation processes.