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English(EN) Knowledge Distillation During Mid-Training Favors Reasoning over Factual Recall

大型语言模型跨语言知识迁移有限，新的蒸馏方法偏重推理 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了大型语言模型如何获取和保留知识。第一篇论文研究了跨语言的事实知识迁移，发现模型从英语到波斯语的迁移有限，尤其是在训练数据中移除了特定事实时。第二篇论文研究了知识蒸馏技术，提出了“Switch Distillation”，该技术通过基于教师置信度和预测熵进行路由，在训练中期偏重推理而非事实回忆。 AI

影响 这些研究突显了当前大型语言模型知识获取的局限性，并提出了改进推理能力的新方法，可能影响未来的模型开发。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了关于大型语言模型知识获取和蒸馏的新研究发现。

在 arXiv cs.CL 阅读 →

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大型语言模型跨语言知识迁移有限，新的蒸馏方法偏重推理 · 跟踪2个来源

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两篇在arXiv上发表的学术论文，详细介绍了关于大型语言模型知识获取和蒸馏的新研究发现。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Romina Oji, Marc Braun, Marcel Bollmann, Marco Kuhlmann, Jenny Kunz ·

    通过训练数据干预探究事实知识迁移

    arXiv:2609.01341v1 Announce Type: cross Abstract: Do multilingual language models transfer factual knowledge across languages during continued pretraining, or do they mostly recall facts learned directly from the target-language data? To answer this question more reliably, we pro…

  2. arXiv cs.CL TIER_1 English(EN) · Jacqueline He, Howard Yen, Shuyue Stella Li, Margaret Li, Hanqing Zeng, Yinglong Xia, Benyu Zhang, Zhuokai Zhao, Qiang Zhang, Pang Wei Koh, Luke Zettlemoyer, Wen-tau Yih ·

    训练中期知识蒸馏偏重推理而非事实回忆

    arXiv:2609.01532v1 Announce Type: new Abstract: Logit-based knowledge distillation (KD) is used to train smaller language models (LMs) via supervision from stronger teachers, but whether its benefits are consistent across training stages remains unclear. Through controlled experi…

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