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Advancing Uto-Aztecan Language Technologies: A Case Study on the Endangered Comanche Language

arXiv 2505.18159 ↗First posted 10 May 2025cs.CL cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 46 references that are in the map. First posted on arXiv 10 May 2025.

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References · 18

  1. 2021Systematic Inequalities in Language Technology Performance across the World's Languagescited by 120
  2. 2021Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learnerscited by 115
  3. 2020Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpuscited by 77
  4. 2024Are LLM-Judges Robust to Expressions of Uncertainty? Investigating the effect of Epistemic Markers on LLM-based Evaluationcited by 29
  5. 2024Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Researchcited by 27
  6. 2024Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Modelscited by 23
  7. 2024MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversationscited by 21
  8. 2022Contrastive Learning for Prompt-Based Few-Shot Language Learnerscited by 17
  9. 2024Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translationcited by 12
  10. 2024Can LLMs Help Create Grammar?: Automating Grammar Creation for Endangered Languages with In-Context Learningcited by 8
  11. 2024Enhanced Detection of Conversational Mental Manipulation Through Advanced Prompting Techniquescited by 7
  12. 2024SAGED: A Holistic Bias-Benchmarking Pipeline for Language Models with Customisable Fairness Calibrationcited by 6
6 more
  1. 2024Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and Challengescited by 6
  2. 2024The Computational Anatomy of Humility: Modeling Intellectual Humility in Online Public Discoursecited by 3
  3. 2024ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Sentencecited by 3
  4. 2025Is It Navajo? Accurate Language Detection in Endangered Athabaskan Languagescited by 3
  5. 2021A Digital Corpus of St. Lawrence Island Yupikcited by 1
  6. 2024Dialectal and Low-Resource Machine Translation for Aromaniancited by 1

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