Visibility as Survival: Generalizing NLP for Native Alaskan Language Identification (2025)

Abstract: Indigenous languages remain largely invisible in commercial language identification (LID) systems, a stark reality exemplified by Google Translate’s LangID tool, which supports over 100 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 create 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 prompting on proprietary and open-source LLMs, achieving nearly perfect accuracy with just 40 examples per language. While initial zero-shot attempts show 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.

## introduction the exclusion of indigenous languages from mainstream nlp technologies @xcite reflects a systemic bias in language technology: the prioritization of highresource languages at the expense of linguistic diversity @xcite . nowhere is this marginalization more evident than in alaska, a north american region home to 20 native alaskan languages ## native alaskan language landscape overview alaska is home to 20 indigenous languages @xcite , each carrying deep cultural, historical, and epistemological significance @xcite . as shown in figure 3 , these languages can be categorized into four major families: inuit-aleut, athabaskan-eyak-tlingit (aet), haida, and tsimshianic. the inuit-aleut family (allen and crago, 1992), spoken across the arctic regions of alaska, canada, and greenland, includes languages such as inupiaq and central alaskan yup'ik, which are polysynthetic, encoding rich semantic information within complex word structures. the aet family @xcite , which shares linguistic ancestry with navajo and other athabaskan languages of the southwest, includes gwich'in and dena'ina, which feature intricate tone systems and highly agglutinative morphology. haida (martineau, 2002), spoken in southeast alaska, remains linguistically isolated, with debates over its classification, while tsimshianic languages (forbes, 2023), such as coast tsimshian and sm'álgyax, exhibit complex verbal morphology and sound systems distinct from neighboring families. figure 5 shows a overall geographical distribution of this languages on a map of alaska. endangered status all 20 native alaskan languages are endangered @xcite , as visualized in figure 4 . eyak is extinct (naeemur rehman and abbas, 2024), while haida, tsimshian, and tlingit are critically endangered @xcite , spoken by only a handful of elderly speakers. dena'ina, gwich'in, tanacross, ## native alaskan languages dataset challenges of existing resources existing datasets for native alaskan languages are scarce, with most existing linguistic resources being limited to online dictionaries @xcite , and small corpora from language preservation initiatives. some documentation exists through university archives (coronado and zavalina, 2024), language revitalization programs @xcite , and community-driven efforts (lidubwi and ndavula, 2025), but these are often fragmented and not readily accessible for computational use. the absence of standardized, publicly available corpora has hindered progress in nlp for these languages. ## applicability and community feedback the ability to accurately identify endangered languages extends beyond nlp research. it is fundamental to digital preservation, automated transcription, and educational tools for revitalization. by demonstrating that large language models can recognize native alaskan languages with minimal supervision, we lay the groundwork for automatic subtitling, real-time translation, and indigenous language integration in voice assistants, ensuring their presence in the digital age. yet, dignity and recognition are just as vital as technological progress @xcite . language is identity, and its digital presence affirms its legitimacy and survival. to uphold transparency, ethics, and community respect @xcite , we engage with native alaskan speakers through informal discussions and three formal interviews to seek guidance on our research direction. interviewees were compensated, and with full permission, one transcript (with identifying information redacted) is included in appendix h, featuring a kenaitze indian tribe member from anchorage, ak. community members reacted with shock and frustration, assuming that major commercial language technologies would already support at least some native languages, especially widely spoken ones like navajo, yet they found no such inclusion. many express that if a small team with limited resources could develop a working language identification system, then large corporations with far greater infrastructure could do so effortlessly, but have simply chosen not to. one of our team members likened this deliberate exclusion to creating a map while knowingly erasing entire regions where people live. this absence of technological support not only marginalizes these languages but also reinforces the systemic neglect that has historically threatened their survival. beyond this study, we are in discussions with a commercial language translation company specializing in endangered languages, whose fairly compensated linguists ensure that knowledge is shared with respect and care. while we hope to actively collaborate, human validation remains costly, and sustainable preservation efforts require institutional support. greater visibility for this work is essential, not just to advance research but to foster the recognition and funding necessary to make meaningful, long-term contributions. by demonstrating the feasibility of lightweight, generalizable approaches, we hope to inspire broader engagement and investment in indigenous language preservation, ensuring that these languages remain not just studied, but actively supported in the digital age. some reflection of the real-life applicability of our work is featured in appendix b. additional cultural and linguistic context for the native alaskan languages covered is provided in appendix g. ## future work while this study establishes the effectiveness of few-shot prompting for native alaskan language identification, several avenues remain for future exploration. expanding this approach to other indigenous and endangered language families, such as athabaskan languages beyond alaska or other arctic and circumpolar languages, would further test its scalability and reinforce its generalizability (appendix f). additionally, future work should focus on developing more sophisticated techniques specifically tailored to native alaskan languages, such as incorporating linguistic structure, phonetic features, or community-driven validation methods to refine model performance. beyond identification, integrating llms into active language revitalization efforts, including machine translation, speech synthesis, and interactive learning tools, could support broader accessibility and digital engagement. ultimately, our findings challenge the assumption that resource-intensive methods are necessary for language technology development. instead, we demonstrate that lightweight, targeted approaches can empower underrepresented languages, paving the way for a more inclusive and linguistically diverse nlp landscape. ## conclusion the exclusion of native alaskan languages from commercial language technologies is not due to insurmountable technical barriers but rather a lack of initiative. while discussions on language preservation are widespread, few efforts translate into tangible action. we show that generalizable nlp approaches, such as few-shot prompting with llms and fine-tuned classification with xlm-roberta, can effectively identify these languages without requiring extensive data or corporate-scale resources. critically, action does not have to be costly or unattainable. in an academic setting, lightweight, targeted approaches can make a significant impact, and our results show that even minimal supervision enables high-accuracy language identification. the continued neglect of these languages in mainstream nlp is a choice, not a necessity. if a small team with limited resources can make meaningful progress, then large-scale institutions and research communities have no excuse for inaction. the level of interest in endangered language technology makes this work not only relevant but highly achievable. generalizable nlp methods provide a viable pathway for expanding linguistic inclusion, and there is no reason this work should not be extended further. we call on the nlp community to move beyond discussion and take concrete steps, whether by expanding datasets, refining models, or collaborating with indigenous speakers, to ensure that these languages are not just studied, but actively supported. by acknowledging and integrating indigenous languages into technological spaces, we take a necessary step toward recognition, revitalization, and digital survival.