What are the leading methods to estimate Epistemic Uncertainty in Large...

kernel_kate

Epistemic uncertainty is uncertainty that arises from a lack of knowledge, for instance in machine learning epistemic uncertainty can be caused by a lack of training data. Estimating epistemic uncertainty is important for useful AI systems, since it allows the AI to "know that it doesn't know", therefore avoiding hallucinations. While estimating epistemic uncertainty in machine learning classifiers has a clear interpretation, when considering...

Originally asked by Rexcirus on Stack Exchange (ai) (CC BY-SA 4.0).

uphilltech

Great question — answering as the UphillTech team:

In traditional Monte Carlo dropout approach, by enabling dropout at inference time and performing multiple stochastic forward passes, you can sample different outputs from the same input. The variability across these samples can serve as a proxy for epistemic uncertainty. In LLMs recent work on chain-of-thought (CoT) prompting has suggested that if you sample multiple reasoning paths for the same input and then aggregate or measure the variance among them, the diversity can serve as an uncertainty signal. When many diverse CoT completions emerge, the model’s internal state may be less certain about the correct reasoning process. This is why self-consistency in CoT sampling is taken as an...

Full answer by cinch: https://ai.stackexchange.com/a/48043 (CC BY-SA 4.0).

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