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English(EN) Multilingual GSM-Symbolic: What determines capability transfer across languages?

新数据集量化了15种语言的AI能力迁移

作者 PulseAugur 编辑部 · [1 个来源] ·

引入了一个名为Multilingual GSM-Symbolic的新数据集，用于研究AI模型中能力如何在不同语言之间迁移。该数据集包含15种语言的30,000个匹配的问答对，研究表明模型规模、语言资源水平和推理能力是跨语言性能的关键预测因素。研究结果表明，更大的模型和更强的推理能力有助于缩小低资源语言和高资源语言之间的性能差距，这对模型开发和评估策略具有启示意义。 AI

影响 提供了一个更好的理解和预测AI模型跨语言性能的框架，可能降低评估成本。

排序理由 该集群包含一篇介绍AI研究新数据集和分析框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新数据集量化了15种语言的AI能力迁移

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该集群包含一篇介绍AI研究新数据集和分析框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kenneth Enevoldsen, Riley Herchert, Sofie Mosegaard, Dan Saattrup Smart, Simon Enni, Isaac Chung, Sofie Bruun, Ayush Sunil Munot, Max M\"uller-Eberstein, Adnan El-Assadi, Elisa Bassignana, Gianluca Barmina, Hafsteinn Einarsson, Iben Nyholm Debess, Linda … ·

    多语言GSM-Symbolic：跨语言能力迁移的决定因素是什么？

    arXiv:2610.03367v1 Announce Type: new Abstract: We understand little about how capabilities acquired in one language carry over to another, or what governs this transfer: evaluations rely on incomparable, saturation-prone datasets and rarely examine its determinants jointly. Iden…

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