Consensus Label

Consensus labeling addresses the challenge of aggregating multiple, potentially conflicting labels for a single data point, often arising from crowdsourced annotation or diverse data sources. Current research focuses on developing robust algorithms, including Bayesian models and weighted ensemble methods, to infer reliable consensus labels while accounting for annotator biases and data heterogeneity; techniques like iterative multi-step decoding in time series forecasting and prompt-feature consensus in vision-language models are also being explored. Accurate consensus labeling is crucial for improving the quality and reliability of training data in machine learning, leading to more effective and unbiased models across various applications, from healthcare diagnostics to natural language processing.