Visibility as Survival: Generalizing NLP for Native Alaskan Language Identification

Indigenous languages remain largely invisible in commercial language identification systems—a stark reality exemplified by Google Translate's LangID tool, which supports 109 languages but excludes all 150 Indigenous languages of North America. This technological marginalization is particularly acute for Alaska's 20 Native languages, all of which face endangerment despite their rich linguistic heritage. We present GenAlaskan, a framework demonstrating how both large language models and specialized classifiers can effectively identify these languages with minimal data. Working closely with Native Alaskan community members, we created Akutaq-2k, a carefully curated dataset of 2000 sentences spanning all 20 languages—named after the traditional Yup'ik dessert symbolizing the blending of diverse elements. We design few-shot learning on proprietary and open-source LLMs, achieving nearly 100% accuracy with just 40 examples per language. While initial zero-shot attempts showed limited success, our systematic attention head pruning revealed critical architectural components for accurate language differentiation, providing insights into model decision-making for low-resource languages. Our results challenge the notion that effective Indigenous language identification requires massive resources or corporate infrastructure, demonstrating that targeted technological interventions can drive meaningful progress in preserving endangered languages in the digital age.