检测大模型对西班牙语地域词汇差异的掌握程度，发现存在数字语言偏见。
Digital Linguistic Bias in Spanish: Evidence from Lexical Variation in LLMs
- 用问答形式测试大模型对21国西班牙语词汇差异的认知能力。
- 西班牙、墨西哥等地词汇识别准确，智利方言最难区分。
- 数据量非主因，揭示了模型内在的数字语言偏见问题。
本研究考察大型语言模型（LLMs）对西班牙语地理词汇差异的捕捉程度，该语言存在显著区域差异。将LLMs视为虚拟信息提供者，我们采用两种问卷式问题格式（是/否与多选题）进行探测，并利用大规模、专家标注的西班牙语词汇差异数据库。评估覆盖21个西班牙语国家的900多个词汇项目，分别在国家和方言区层面展开。两种评测格式均显示，模型对西班牙、赤道几内亚、墨西哥及中美洲、拉普拉塔河地区的词汇差异识别更准确，而智利方言则尤其难以区分。重要的是，国家级数字资源数量差异无法解释这些表现模式，表明数据量之外的因素塑造了模型中的方言表征。本研究通过细粒度、大规模评估，推进了对大模型方言知识的理解，为西班牙语数字语言偏见讨论提供了新证据。
原文摘要 · Abstract (English)
This study examines the extent to which Large Language Models (LLMs) capture geographic lexical variation in Spanish, a language that exhibits substantial regional variation. Treating LLMs as virtual informants, we probe their dialectal knowledge using two survey-style question formats: Yes-No questions and multiple-choice questions. To this end, we exploited a large-scale, expert-curated database of Spanish lexical variation. Our evaluation covers more than 900 lexical items across 21 Spanish-speaking countries and is conducted at both the country and dialectal area levels. Across both evaluation formats, the results reveal systematic differences in how LLMs represent Spanish language varieties. Lexical variation associated with Spain, Equatorial Guinea, Mexico & Central America, and the La Plata River is recognized more accurately by the models, while the Chilean variety proves particularly difficult for the models to distinguish. Importantly, differences in the volume of country-level digital resources do not account for these performance patterns, suggesting that factors beyond data quantity shape dialectal representation in LLMs. By providing a fine-grained, large-scale evaluation of geographic lexical variation, this work advances empirical understanding of dialectal knowledge in LLMs and contributes new evidence to discussions of Digital Linguistic Bias in Spanish.
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