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English(EN) Beyond LLMs: The Rise of Foundation Models for Tables, Time Series, and Structured Data

基础模型已超越语言,扩展至结构化数据

基础模型这一概念曾主要由大型语言模型主导,如今正扩展到涵盖表格数据、时间序列和其他结构化数据格式。虽然TabPFN和TimesFM等模型展示了跨这些多样化数据集进行泛化的能力,但关键挑战在于知识迁移到下游任务的程度。与语言不同,结构化数据由于列含义各异和模式不一致而带来独特的困难,需要模型通过上下文进行适应,而不是仅仅依赖预训练。 AI

影响 结构化数据的基础模型可以通过上下文适应而非完全重新训练来简化机器学习系统的开发。

排序理由 该条目讨论了结构化数据基础模型的发展和应用,这是一个面向研究的主题。[lever_c_demoted from research: ic=1 ai=1.0]

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基础模型已超越语言,扩展至结构化数据

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该条目讨论了结构化数据基础模型的发展和应用,这是一个面向研究的主题。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Towards AI TIER_1 English(EN) · Deepanshu Gupta ·

    超越LLMs:表格、时间序列和结构化数据的基础模型崛起

    <h4>Foundation models are now being developed for tables, time series, and other structured data. The important question is not what we call them, but whether they can make machine-learning systems faster and easier to build.</h4><figure><img alt="" src="https://cdn-images-1.medi…