English(EN) MIXAR: Scaling Autoregressive Pixel-based Language Models to Multiple Languages and Scripts

MIXAR：首个生成式多语言像素语言模型发布

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了MIXAR，一种新颖的生成式多语言像素语言模型，其参数量可扩展至5亿，并在八种高资源语言上进行了预训练。该模型在对未见过的低资源语言（尤其是与预训练混合语言文字差异较大的语言）上表现出更强的泛化能力。MIXAR用基于GRPO的强化学习阶段取代了不稳定的对抗性训练，实现了更稳定的自由生成，在多语言基准测试上的准确率提高了2.3倍。该模型在区分性任务上表现与基于分词器的模型相当，并且对拼写攻击的抵抗力更强。 AI

影响该模型有望通过提高低资源语言的性能并提供一种比基于分词器模型更稳定的替代方案，来促进公平的语言技术发展。

排序理由这是一篇研究论文，详细介绍了新的模型架构和训练方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Chen Hu, Yintao Tai, Antonio Vergari, Frank Keller, Alessandro Suglia · 2026-10-16 04:00

MIXAR：将自回归像素语言模型扩展到多种语言和脚本

arXiv:2604.11575v2 Announce Type: replace Abstract: Pixel-based language models are gaining momentum as alternatives to traditional token-based approaches, promising to circumvent tokenization challenges; however, existing models remain English-centric and are hard to train as ge…

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报道来源 [1]

MIXAR：将自回归像素语言模型扩展到多种语言和脚本

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