首个针对濒危语言康麦奇语的计算研究，用少量数据提升大模型识别准确率。
Advancing Uto-Aztecan Language Technologies: A Case Study on the Endangered Comanche Language
- 构建412条短语的手工数据集，结合合成数据增强模型训练。
- 仅用5个示例的少样本提示，使GPT-4o实现接近100%的识别准确率。
- 强调社区参与与文化敏感性，为濒危语言提供可复制的数字保护路径。
濒危语言在自然语言处理中的数字排斥仍是重大挑战，制约语言学研究与复兴工作。本研究首次对濒临灭绝的乌托-阿兹特克语系康麦奇语开展计算探索，展示了低成本、社区参与型NLP干预如何助力语言保存。我们构建了一个包含412条短语的手工标注数据集，设计了一套合成数据生成流程，并对GPT-4o与GPT-4o-mini进行了语言识别的实证评估。实验表明，尽管大模型在零样本设置下表现不佳，但通过少样本提示（仅需5个示例）显著提升性能，达到近乎完美的准确率。研究凸显了针对性NLP方法在低资源场景下的潜力，强调可见性是实现包容的第一步。通过为康麦奇语建立NLP基础，本文倡导以可访问性、文化敏感性和社区协作为核心的计算路径。
原文摘要 · Abstract (English)
The digital exclusion of endangered languages remains a critical challenge in NLP, limiting both linguistic research and revitalization efforts. This study introduces the first computational investigation of Comanche, an Uto-Aztecan language on the verge of extinction, demonstrating how minimal-cost, community-informed NLP interventions can support language preservation. We present a manually curated dataset of 412 phrases, a synthetic data generation pipeline, and an empirical evaluation of GPT-4o and GPT-4o-mini for language identification. Our experiments reveal that while LLMs struggle with Comanche in zero-shot settings, few-shot prompting significantly improves performance, achieving near-perfect accuracy with just five examples. Our findings highlight the potential of targeted NLP methodologies in low-resource contexts and emphasize that visibility is the first step toward inclusion. By establishing a foundation for Comanche in NLP, we advocate for computational approaches that prioritize accessibility, cultural sensitivity, and community engagement.
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