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This paper evaluates the reliability of BERT-based QA models (RoBERTa, ALBERT, DistilBERT) under Monte Carlo Dropout and input paraphrasing, finding RoBERTa more consistent and validating MCD as a reliability metric.
This paper shows that Monte Carlo dropout provides epistemic uncertainty signals for chest radiograph classifiers, which improves error detection and reduces confident misdiagnoses in clinical decision-support agents when communicated as a binary error-risk flag.
This paper presents a comparative study of Monte Carlo Dropout and Deep Ensemble methods for uncertainty quantification in AI-driven crash simulation surrogates, using an open-source bumper beam benchmark.
This paper argues that reward models in RL are often oversensitive, assigning different scores to equally good responses, and proposes a training-free discretization algorithm using Monte Carlo dropout to reduce oversensitivity, improving policy quality.
This paper identifies oversensitivity in continuous reward models for reinforcement learning, where equally good responses receive different scores, and proposes a discretization technique using Monte Carlo dropout to reduce this oversensitivity while maintaining discriminative ability, leading to better policies and less reward hacking.