English(EN) AQL-Lite: Enabling Efficient Knowledge Acquisition for Arabic LLMs via Data-Centric Continual Pretraining

新框架增强阿拉伯语大语言模型的知识获取能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员开发了AQL-Lite框架，旨在高效地使多语言大语言模型适应现代标准阿拉伯语（MSA）。该方法采用质量优先的数据策管线和部分参数持续预训练（CPT）技术。实验表明，与使用原始网络文本相比，在AQL策管的АQL-Lite数据上进行CPT显著提高了阿拉伯语理解能力，并且一个规模化模型在Open Arabic LLM Leaderboard v2上取得了高分。 AI

影响这项研究为开发高性能的阿拉伯语模型提供了更有效的途径，可能增加阿拉伯语地区的可访问性和实用性。

排序理由该集群包含一篇学术论文，详细介绍了一种使大语言模型适应特定语言的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Houssam Eddine Boukhalfa, Mourad Bouache, Mourad Dahmane · 2026-10-23 04:00

AQL-Lite: Enabling Efficient Knowledge Acquisition for Arabic LLMs via Data-Centric Continual Pretraining

arXiv:2610.30344v1 Announce Type: new Abstract: Training Arabic large language models faces challenges from noisy web data, dialectal variation, and the high cost of continual pretraining (CPT). We introduce AQL-Lite (AQL: Arabic Quality Language), a time- and resource-efficient …

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AQL-Lite: Enabling Efficient Knowledge Acquisition for Arabic LLMs via Data-Centric Continual Pretraining

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