English(EN) In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning

论文将上下文学习与贝叶斯推理和元学习联系起来

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-16 04:00

一篇新论文提出了一个统计理论，在元学习框架内解释上下文学习（ICL）。该理论将ICL风险分解为贝叶斯差距（衡量模型逼近最优预测器的程度）和后验方差（代表内在任务不确定性）。对于Transformer，该论文推导出的界限表明，任务混合引起的不确定性随少量示例迅速减小，而贝叶斯差距取决于预训练提示和上下文长度。 AI

排序理由学术论文发布在arXiv上，详细介绍了上下文学习的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

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Newsworthiness bucket: Tool; 学术论文发布在arXiv上，详细介绍了上下文学习的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.LG TIER_1 English(EN) · Tomoya Wakayama, Taiji Suzuki · 2026-06-16 04:00

上下文学习可被证明是贝叶斯推理：元学习的泛化理论

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…

本文如何被排名

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上下文学习可被证明是贝叶斯推理：元学习的泛化理论

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