#risk-decomposition News & Analysis

MiRD: Reliable Set-Valued Prediction for Open-Ended Question Answering via Miscoverage Risk Decomposition

Researchers introduce MiRD, a two-stage framework that improves reliable prediction for open-ended question answering by separately addressing sampling failures and selection errors. The approach maintains calibration-set integrity while controlling hallucinations in AI models, outperforming existing conformal prediction methods across multiple datasets and models.