推进公平人工智能：评估大语言模型在拉丁美洲语境下的文化表达力
Advancing Equitable AI: Evaluating Cultural Expressiveness in LLMs for Latin American Contexts
AI总结：
本文通过构建植根于拉丁美洲历史与社会政治语境的文化感知数据集并提出文化表达力指标，评估并微调大语言模型，使Mistral-7B的文化表达力提升42.9%，以推进公平AI发展。
摘要：
人工智能（AI）系统往往反映经济发达地区的偏见，由于数据集不平衡，拉丁美洲等经济欠发达地区的语境被边缘化。本文考察了AI对多样化拉丁美洲语境的表征，揭示了经济发达地区与欠发达地区数据之间的差异。我们强调英语相对于西班牙语、葡萄牙语以及克丘亚语和纳瓦特尔语等原住民语言的主导地位如何延续偏见，并以西方视角框定拉丁美洲的观点。为解决这一问题，我们引入了一个植根于拉丁美洲历史和社会政治语境的文化感知数据集，以挑战欧洲中心主义模型。我们使用一种新颖的文化表达力（Cultural Expressiveness）指标、统计检验和语言分析，在测试文化语境意识的问题上评估了六个语言模型。我们的研究结果表明，一些模型能更好地捕捉拉丁美洲的观点，而另一些模型则表现出显著的情感错位（p < 0.001）。使用我们的数据集对Mistral-7B进行微调，使其文化表达力提升了42.9%，推动了公平AI的发展。我们倡导公平AI，优先采用反映拉丁美洲历史、原住民知识和多样语言的数据集，同时强调以社区为中心的方法来放大边缘化群体的声音。
英文摘要：
Artificial intelligence (AI) systems often reflect biases from economically advanced regions, marginalizing contexts in economically developing regions like Latin America due to imbalanced datasets. This paper examines AI representations of diverse Latin American contexts, revealing disparities between data from economically advanced and developing regions. We highlight how the dominance of English over Spanish, Portuguese, and indigenous languages such as Quechua and Nahuatl perpetuates biases, framing Latin American perspectives through a Western lens. To address this, we introduce a culturally aware dataset rooted in Latin American history and socio-political contexts, challenging Eurocentric models. We evaluate six language models on questions testing cultural context awareness, using a novel Cultural Expressiveness metric, statistical tests, and linguistic analyses. Our findings show that some models better capture Latin American perspectives, while others exhibit significant sentiment misalignment (p < 0.001). Fine-tuning Mistral-7B with our dataset improves its cultural expressiveness by 42.9%, advancing equitable AI development. We advocate for equitable AI by prioritizing datasets that reflect Latin American history, indigenous knowledge, and diverse languages, while emphasizing community-centered approaches to amplify marginalized voices.
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。
