用大模型分析卢森堡新闻评论中的语言意识形态，揭示小语种下身份认同的深层社会意义。
Language Ideologies in a Multilingual Society: An LLM-based Analysis of Luxembourgish News Comments
- 人工标注卢森堡语评论，构建多类别语言意识形态数据集
- 小语种数据导致模型性能受限，翻译到高资源语言后效果提升
- 为跨语言社会话语分析提供可落地的自动化工具
检测语言意识形态是理解话语如何建构身份的重要但复杂任务。在多元文化、多语并存的卢森堡社会，语言意识形态不仅反映语言偏好，更承载深层文化与社会意义，影响身份认同与社会归属感。本文结合自然语言处理与社会语言学最新进展，探索大语言模型在识别语言意识形态方面的潜力。研究团队手动标注了一组卢森堡语用户评论，涵盖预定义的语言意识形态类别，并在不同提示条件下评估大模型的分类表现。鉴于卢森堡语属于小语种且在主流大模型训练数据中代表性不足，研究进一步检验将数据机器翻译至高资源语言是否能提升任务性能。结果表明，尽管大模型尚未完全适配多类别意识形态标注任务，但在实际应用中已具备识别语言意识形态内容的可行性。
原文摘要 · Abstract (English)
Detecting language ideologies is a valuable yet complex task for understanding how identities are constructed through discourse. In Luxembourg's multicultural and multilingual society, language ideologies reflect more than simple preferences: they carry deep cultural and social meanings, shaping identities and social belonging. Following recent developments in applying Natural Language Processing tools to linguistics and social science, this paper explores the potential of large language models to assist in the detection of language ideologies. We manually annotate a corpus of user comments in Luxembourgish with predefined ideological categories and then evaluate the performance of large language models under varying prompt conditions to assess their ability to replicate these human annotations. Since Luxembourgish is a small language and poorly represented in the LLMs' training data, we also investigate whether machine-translating the data to high-resource languages increases performance on the ideology detection task. Our findings suggest that, while LLMs are not yet fully optimized for a multi-class ideological annotation task, they are practical tools to identify language ideological content.
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