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New framework defines "any-task inference" for foundation models

研究人员提出了一个形式化框架,用于定义和构建能够进行“任何任务推理”(ATI)的基础模型,这是一种类似于零样本推理的能力。该框架将 ATI 模型在数学上定义为能够将数据样本和任务直接映射到预测,而无需进行特定任务的微调。拟议的将 ATI 模型分解为编码器、模型和解码器方面,允许将模型误差分解为理论设计项和已实现模型过剩项。该论文还提出了两种创建 ATI 基础模型的原型方法:生成式和判别式变体,并通过五个实际示例进行了说明。 AI

影响 为开发能够处理各种任务而无需特定微调的更通用的 AI 模型提供了理论基础。

排序理由 该条目是一篇发表在 arXiv 上的学术论文,详细介绍了基础模型的理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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New framework defines "any-task inference" for foundation models

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该条目是一篇发表在 arXiv 上的学术论文,详细介绍了基础模型的理论框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Matthew McDermott, Florent Pollet, Payal Chandak, Gregory Kondas, Liat Friedman Antwarg, Noa Dagan, Isaac Kohane ·

    A Theoretical Framework for Understanding and Building "Any-task inference" Capable Foundation Models

    arXiv:2610.40934v1 Announce Type: new Abstract: In this work, we provide a formal framework and mathematical definition for the subset of foundation models focused on enabling the property of "any-task inference" (ATI) (similar to zero-shot inference), rather than those designed …