评测轻量大模型在伊比利亚语言零样本任务中的表现
Evaluating Compact LLMs for Zero-Shot Iberian Language Tasks on End-User Devices
- 选用最新轻量级大模型，针对伊比利亚语种设计多任务评估
- 部分模型在特定任务上表现优异，但巴斯克语等语言仍有显著差距
- 适合关注低资源语言、边缘设备部署的研究者和开发者
大型语言模型在自然语言生成、翻译和推理等任务中取得显著进展，但其巨大的计算需求限制了在消费级设备上的部署。这一问题在伊比利亚半岛的低资源语言（如巴斯克语）中尤为突出，因语言资源和基准数据有限，难以有效评估。本文对多个前沿轻量级大模型在面向伊比利亚语言的多项核心NLP任务中进行了全面评估。结果显示，尽管部分模型在某些任务上表现稳定，但在巴斯克语等语言上仍存在明显性能差距。研究强调需进一步探索轻量化与多语言鲁棒性之间的平衡。
原文摘要 · Abstract (English)
Large Language Models have significantly advanced natural language processing, achieving remarkable performance in tasks such as language generation, translation, and reasoning. However, their substantial computational requirements restrict deployment to high-end systems, limiting accessibility on consumer-grade devices. This challenge is especially pronounced for under-resourced languages like those spoken in the Iberian Peninsula, where relatively limited linguistic resources and benchmarks hinder effective evaluation. This work presents a comprehensive evaluation of compact state-of-the-art LLMs across several essential NLP tasks tailored for Iberian languages. The results reveal that while some models consistently excel in certain tasks, significant performance gaps remain, particularly for languages such as Basque. These findings highlight the need for further research on balancing model compactness with robust multilingual performance
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