Proposes self-function vectors and a controlled evaluation protocol to quantify aleatoric uncertainty in ICL separately from epistemic uncertainty for more reliable LLM confidence measures.
Noam Itzhak Levi, Alon Beck, and Yohai Bar-Sinai
2 Pith papers cite this work. Polarity classification is still indexing.
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Epistemic uncertainty collapses sharply at grokking in in-context learning transformers, serving as a diagnostic of delayed generalization and linked via a Bayesian linear model to a shared spectral mechanism.
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Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence
Proposes self-function vectors and a controlled evaluation protocol to quantify aleatoric uncertainty in ICL separately from epistemic uncertainty for more reliable LLM confidence measures.
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A Bayesian Perspective on the Role of Epistemic Uncertainty for Delayed Generalization in In-Context Learning
Epistemic uncertainty collapses sharply at grokking in in-context learning transformers, serving as a diagnostic of delayed generalization and linked via a Bayesian linear model to a shared spectral mechanism.