针对西语区方言差异，提出本地化AI模型优化用户参与度。
Crossing Borders Without Crossing Boundaries: How Sociolinguistic Awareness Can Optimize User Engagement with Localized Spanish AI Models Across Hispanophone Countries
- 构建五个西班牙语子变体以适配不同西语区
- 方言差异导致用户信任度下降，本地化可缓解
- 适合关注跨区域AI落地与文化包容性的团队
大型语言模型本质上基于语言。为强调区域性本地化模型的必要性，本文深入分析拉丁美洲与西班牙书面西班牙语的主要差异，并进行社会文化与语言背景的深度阐释。我们指出，这些差异在日常使用中形成显著的社会语言鸿沟，导致不同方言群体间出现沟通不适，因此具备地域敏感性的AI模型在弥合这些差距中将发挥关键作用。该方法不仅有助于制定更高效、更具包容性的本地化策略，还能在低风险地理区域实现可持续的日常用户增长。实施至少提出的五个西班牙语子变体，可同时达成两个目标：提升用户对AI语言模型的信任与依赖，同时体现对文化、历史及社会语言学的深刻认知，从而增强国际化的整体形象。
原文摘要 · Abstract (English)
Large language models are, by definition, based on language. In an effort to underscore the critical need for regional localized models, this paper examines primary differences between variants of written Spanish across Latin America and Spain, with an in-depth sociocultural and linguistic contextualization therein. We argue that these differences effectively constitute significant gaps in the quotidian use of Spanish among dialectal groups by creating sociolinguistic dissonances, to the extent that locale-sensitive AI models would play a pivotal role in bridging these divides. In doing so, this approach informs better and more efficient localization strategies that also serve to more adequately meet inclusivity goals, while securing sustainable active daily user growth in a major low-risk investment geographic area. Therefore, implementing at least the proposed five sub variants of Spanish addresses two lines of action: to foment user trust and reliance on AI language models while also demonstrating a level of cultural, historical, and sociolinguistic awareness that reflects positively on any internationalization strategy.
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