从数据匮乏到数据关怀：重新构思塞尔维亚及其他低资源语言的语言技术
From Data Scarcity to Data Care: Reimagining Language Technologies for Serbian and other Low-Resource Languages
AI总结：
本研究提出Data Care框架，旨在通过CARE原则重构低资源语言的语言技术，以解决数据偏见和文化不平等问题。
摘要：
大型语言模型通常在主导语言如英语上进行训练，其对低资源语言的表示通常反映了源语言材料中存在的文化和社会语言学偏见。以塞尔维亚语言为例，本研究探讨了在人工智能时代塑造低资源语言语言技术发展的结构性、历史性和社会技术因素。通过与十位学者和从业者（包括语言学家、数字人文学者和AI开发者）进行半结构化访谈，研究追溯了历史摧毁塞尔维亚文本遗产所带来的挑战，这些挑战被当代问题加剧，导致以工程优先为导向的简化方法，优先考虑功能而非语言细微差别。这些包括表面转写、依赖英语训练模型、数据偏见以及缺乏文化特异性的数据集编纂。为解决这些挑战，本研究提出Data Care框架，其基于CARE原则（集体利益、控制权、责任和伦理），将偏见缓解从事后技术修补重新定位为语料库设计、注释和治理过程中的核心组成部分，并将Data Care定位为在传统LLM开发再现现有权力不平衡和文化盲点的背景下，构建包容性、可持续性和文化扎根的语言技术的可复制模型。
英文摘要：
Large language models are commonly trained on dominant languages like English, and their representation of low resource languages typically reflects cultural and linguistic biases present in the source language materials. Using the Serbian language as a case, this study examines the structural, historical, and sociotechnical factors shaping language technology development for low resource languages in the AI age. Drawing on semi structured interviews with ten scholars and practitioners, including linguists, digital humanists, and AI developers, it traces challenges rooted in historical destruction of Serbian textual heritage, intensified by contemporary issues that drive reductive, engineering first approaches prioritizing functionality over linguistic nuance. These include superficial transliteration, reliance on English-trained models, data bias, and dataset curation lacking cultural specificity. To address these challenges, the study proposes Data Care, a framework grounded in CARE principles (Collective Benefit, Authority to Control, Responsibility, and Ethics), that reframes bias mitigation from a post hoc technical fix to an integral component of corpus design, annotation, and governance, and positions Data Care as a replicable model for building inclusive, sustainable, and culturally grounded language technologies in contexts where traditional LLM development reproduces existing power imbalances and cultural blind spots.
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。
