Variational Uncertainty Decomposition for In-Context Learning

• URL: http://arxiv.org/abs/2509.02327v2
• Date: Wed, 03 Sep 2025 10:56:13 GMT
• Title: Variational Uncertainty Decomposition for In-Context Learning
• Authors: I. Shavindra Jayasekera, Jacob Si, Filippo Valdettaro, Wenlong Chen, A. Aldo Faisal, Yingzhen Li,
• Abstract summary: We introduce a variational uncertainty decomposition framework for in-context learning.<n>We optimise auxiliary queries as probes to obtain an upper bound to the aleatoric uncertainty of an in-context learning procedure.
• Score: 16.986925734554447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models (LLMs) gain popularity in conducting prediction tasks in-context, understanding the sources of uncertainty in in-context learning becomes essential to ensuring reliability. The recent hypothesis of in-context learning performing predictive Bayesian inference opens the avenue for Bayesian uncertainty estimation, particularly for decomposing uncertainty into epistemic uncertainty due to lack of in-context data and aleatoric uncertainty inherent in the in-context prediction task. However, the decomposition idea remains under-explored due to the intractability of the latent parameter posterior from the underlying Bayesian model. In this work, we introduce a variational uncertainty decomposition framework for in-context learning without explicitly sampling from the latent parameter posterior, by optimising auxiliary queries as probes to obtain an upper bound to the aleatoric uncertainty of an LLM's in-context learning procedure, which also induces a lower bound to the epistemic uncertainty. Through experiments on synthetic and real-world tasks, we show quantitatively and qualitatively that the decomposed uncertainties obtained from our method exhibit desirable properties of epistemic and aleatoric uncertainty.

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