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Paper links in-context learning to Bayesian inference and meta-learning

By PulseAugur Editorial · [1 sources] ·

A new paper proposes a statistical theory to explain in-context learning (ICL) within a meta-learning framework. The theory decomposes ICL risk into a Bayes Gap, which measures how well a model approximates the optimal predictor, and Posterior Variance, representing intrinsic task uncertainty. For Transformers, the paper derives bounds showing that uncertainty from task mixtures diminishes rapidly with few examples, while the Bayes Gap depends on pretraining prompts and context length. AI

RANK_REASON Academic paper published on arXiv detailing a new theoretical framework for in-context learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Paper links in-context learning to Bayesian inference and meta-learning

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Academic paper published on arXiv detailing a new theoretical framework for in-context learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tomoya Wakayama, Taiji Suzuki ·

    In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning

    arXiv:2510.10981v3 Announce Type: replace-cross Abstract: This paper develops a finite-sample statistical theory for in-context learning (ICL), analyzed within a meta-learning framework that accommodates mixtures of diverse task types. We introduce a principled risk decomposition…

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