新的语言模型学习分词以提高预训练效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员为 2026 BabyLM 挑战赛开发了两种新的子词分割语言模型：SubSegGPT 和 SubSegDeBERTa。这些模型在预训练阶段学习分词，从而能够发现最优的子词单元。在 Strict 赛道的零样本评估中，SubSegDeBERTa 取得了显著的进步，而在 Strict-small 赛道上，SubSegGPT 的表现优于基于分词的基线模型。研究结果表明，可学习的子词分词提高了语言模型预训练的样本效率。 AI

影响通过学习分词，展示了提高语言模型预训练样本效率的潜力。

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arXiv cs.CL TIER_1 English(EN) · Francois Meyer · 2026-09-02 04:00

Subword Segmental BabyLMs: 学习分词以实现样本高效预训练

arXiv:2609.01151v1 Announce Type: new Abstract: In the standard LM training pipeline, subword tokenisation is applied as a preprocessing step. Subword segmental language modelling is an alternative paradigm in which tokenisation is learned during training, allowing the model to d…

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Subword Segmental BabyLMs: 学习分词以实现样本高效预训练

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