探索多语言建模的迷宫
Exploring the Maze of Multilingual Modeling
AI总结：
本文全面评估了mBERT、XLM-R和GPT-3在文本分类与生成任务上的表现，发现预训练数据量、通用资源可用性、语系和文字类型是影响多语言模型性能的重要因素。
摘要：
近年来，多语言语言模型受到了广泛关注，使得能够满足不同语言环境的应用开发成为可能。本文对三种流行的多语言语言模型：mBERT、XLM-R和GPT-3进行了全面评估。我们在多种语言上评估了它们的性能，重点在两项不同的任务——文本分类和文本生成下，理解资源可用性（通用和模型特定）、语系、文字类型和词序对模型性能的影响。我们的发现表明，虽然特定语言的预训练数据量在模型性能中起着至关重要的作用，但我们还确定了其他因素，如通用资源可用性、语系和文字类型，作为重要特征。我们希望我们的研究有助于深入理解多语言语言模型，以提升其在不同语言和语言环境下的性能。
英文摘要：
Multilingual language models have gained significant attention in recent years, enabling the development of applications that meet diverse linguistic contexts. In this paper, we present a comprehensive evaluation of three popular multilingual language models: mBERT, XLM-R, and GPT-3. We assess their performance across a diverse set of languages, with a focus on understanding the impact of resource availability (general and model-specific), language family, script type, and word order on model performance, under two distinct tasks - text classification and text generation. Our findings reveal that while the amount of language-specific pretraining data plays a crucial role in model performance, we also identify other factors such as general resource availability, language family, and script type, as important features. We hope that our study contributes to a deeper understanding of multilingual language models to enhance their performance across languages and linguistic contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
