Advancing Uto-Aztecan Language Technologies: A Case Study on the Endangered Comanche Language
It addresses the problem of endangered language preservation for linguistic researchers and communities, though it is incremental as it applies existing methods to a new language.
This study tackled the digital exclusion of the endangered Comanche language by introducing the first computational investigation, using minimal-cost NLP interventions to support preservation, and found that few-shot prompting with GPT-4o models significantly improved language identification accuracy to near-perfect levels with just five examples.
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.