西班牙语不仅仅是一个: 一个数据集的西班牙语方言识别为LLMs
Gonzalo Martínez1, Marina Mayor-Rocher2, Cris Pozo Huertas1
1Information Processing and Telecommunications Center (IPTC), Universidad Politécnica de Madrid, Avda. Complutense 30, 28040 Madrid, Spain.
Data in brief
|October 10, 2025
概括
这项研究引入了一个新的数据集,用于评估西班牙方言的大型语言模型 (LLM). 该数据集有助于评估LLM在识别不同西班牙语品种的准确性,并识别潜在的方言偏差.
科学领域:
- 计算语言学 计算语言学
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 多语言能力对NLP至关重要,但方言变化被低估了.
- 西班牙语在词汇,形态和语法方面表现出显著的区域多样性,影响了NLP的表现.
- 确保所有西班牙方言的公平代表性对于语言保存和AI公平性至关重要.
研究的目的:
- 引入一套新的数据集,用于评估西班牙方言变异的大型语言模型 (LLM).
- 评估LLM准确识别不同西班牙品种的能力.
- 测量LLM对特定西班牙语方言的潜在偏见或偏好.
主要方法:
- 开发一个包含30个多选题的数据集,重点关注西班牙语方言差异.
- 专家语言审查和反复改进问题,以确保准确性和偏见检测.
- 使用数据集来测试LLM在识别和区分西班牙地区差异方面的表现.
主要成果:
- 该数据集有效地探讨了LLM对西班牙方言细微差别的理解.
- 最初的分析表明,在不同的西班牙方言中,LLM绩效存在差异.
- 该数据集作为一个基准,用于识别和减轻LLMs的方言偏见.
结论:
- 这一数据集是推进西班牙NLP公平和包容性评估框架的关键资源.
- 它促进了对语言多样性更敏感和更适应的LLM的发展.
- 这项工作有助于在AI中公平地代表语言,防止较小方言的边缘化.
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