United States

We present the first systematic benchmark of parameter-efficient fine-tuning (PEFT) for low-resource neural machine translation (NMT) of indigenous languages of the Americas. We evaluate eight PEFT methods alongside full fine-tuning on NLLB-200-distilled-600M across 13 indigenous-to-Spanish language pairs spanning four resource tiers (357-125,008 training sentences). OFT (Orthogonal Finetuning) achieves the highest development-set chrF++ among PEFT methods (26.63) while training only 0.28% of parameters. LoRA (Low-Rank Adaptation) offers a strong efficiency-quality tradeoff (25.27 chrF++, 0.19%). On held-out test data, full fine-tuning ranks first (25.12) with OFT a close second (25.06; p = 0.43). VeRA (Vector-based Random Matrix Adaptation) and Prefix Tuning consistently underperform. These results demonstrate that PEFT is a viable alternative to full fine-tuning for indigenous-language NMT.

ACL 2026

A Systematic Comparison of Parameter-Efficient Fine-Tuning Techniques for Low-Resource Neural Machine Translation: Evidence from Indigenous Languages of the Americas

translation

fine-tuning

low-resource

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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

This paper presents a multi-dialect text classifier for Quechua that augments neural models with rule-based linguistic information to address challenges in low-resource, morphologically complex settings. The approach is built on a carefully curated dataset spanning multiple genres, including annotated parallel bible corpora, and encodes manually annotated lexical variation and polypersonal verbal agreement as explicit features within a transformer-based classifier. Results show that neural models substantially outperform statistical baselines, enabling highly accurate multi-class classification across 27 Quechua dialects. The impact of linguistic augmentation is context-dependent: gains are minimal in high-resource settings but more pronounced in low-resource and cross-domain conditions. Overall, this work aims to contribute to the development of dialect-sensitive NLP methods for Quechua and other low-resource, morphologically rich languages.

Linguistic Feature Tagging for Automatic Classification of 27 Closely-Related Quechua Varieties

Interlinear glossing is essential for the study and revitalization of endangered languages. However, it remains a time-consuming process that requires extensive linguistic expertise. Recent advances in Large Language Models (LLMs) offer a potential solution. In this research, we study the case of Mapudungun, an endangered language spoken in Chile and Argentina, to generate automatic interlinear glosses using the Gemini 2.5 Pro model. Our study investigates which information configuration through Retrieval-Augmented Generation (RAG) yields the best results. We compare the integration of a formal grammar, a dictionary, a small annotated corpus, and a combination of all these resources. Our evaluation shows that while dictionary integration causes a significant degradation in performance, grounding the model with a structured corpus maximizes accuracy relative to the resources employed. Notably, we find that a remarkably small dataset of 589 meaning units provides enough normative guidance to significantly improve the morphological tagging task. This work highlights the viability of utilizing minimally annotated corpora to assist in the documentation of morphologically complex languages.

What Resources Matter for Interlinear Glossing? Using LLMs and RAG for the Low-Resource Mapudungun Language

Large language models (LLMs) struggle with low-resource polysynthetic languages, yet the nature of their failures remains underexplored. We evaluate GPT-4o-mini, Gemma~3~27B, Llama~3.3~70B, and NLLB-200 on Spanish$\leftrightarrow$Wixárika translation using zero-shot and 5-shot prompting. All systems are unusable, scoring below 3 BLEU and 21 chrF. Qualitative analysis reveals that LLMs largely ignore source content and instead generate fluent hallucinations. Spanish outputs frequently include indigenous cultural stereotypes such as deer, deities, rain dance, and shamans, regardless of the input, while Wixárika outputs are repetitive across different inputs and morphologically implausible. Few-shot prompting yields model-dependent improvements, with Gemma and Llama improving substantially at higher shot counts while GPT-4o-mini remains flat. These results demonstrate that current LLMs are unable to represent polysynthetic morphology and instead default to exoticizing Indigenous culture and identity. We call for the development of inclusive morphological-aware modeling strategies and increased resource creation to ensure that Indigenous languages of the Americas are represented safely and accurately.

Deer, Deities, and Dancing: Culturally Biased LLM Hallucination in Low-Resource Wixárika Translation

This paper presents IndigiEval, a framework for evaluating the language and cultural proficiency of several commercially available large language models (LLMs) across five North American Indigenous languages (Mvskoke, Choctaw, Cherokee, Cheyenne, and Hawaiian). This framework is a qualitative evaluation method intended for communities with small speaker populations to be able to critically evaluate LLM performance with minimal data and human effort. IndigiEval includes tasks such as answering cultural questions, translation, text generation, and speech recognition. The results of our experiments indicate that no currently available LLM performs well across all evaluation categories, and that LLMs frequently hallucinate orthographies, grammatical structures, cultural knowledge, and vocabulary for all languages and cultures considered. Our proposed evaluation framework is not intended as a comprehensive score, but rather a qualitative and flexible framework to inform language communities about a given LLM's potential as a resource, since each language has unique environments, strengths, and availability of resources.

IndigiEval: Evaluating LLMs in North American Indigenous Languages

This paper presents a study focused on advancing Automatic Speech Recognition (ASR) for the under-resourced language Dënë Sųłıné through data-centric approaches. We explore multiple strategies to enhance the quality of training data—both audio recordings and transcriptions—to address the challenges posed by mixed-quality datasets. Our experiments investigate which data preparation techniques most effectively improve ASR performance in this context. Our findings show that reducing non-phonemic spelling variation in the corpus significantly improves model generalization, resulting in a substantial increase in recognition accuracy. Additionally, we demonstrate that increasing manually reviewed transcriptions consistently improves word and character error rates, while audio enhancement slightly reduces performance, highlighting the complex trade-offs in low-resource ASR development.

A data-centric approach to performance improvement in under-resourced ASR: The case of Dënë Sųłıné

This paper describes ongoing work to develop a corpus of Cahuilla language from the John Peabody Harrington collection, which contains linguistic and ethnographic fieldnotes documenting Indigenous languages of California and other regions across the Americas. Handwritten notes present numerous processing challenges, including scratch-outs, multilingual entries in Spanish and other Indigenous languages, unique abbreviations, and varying script orientations. We compare the efficacy of deep learning text recognition models to convert images of the notes into a machine-readable format, with a focus on respecting tribal data sovereignty in our methods. We find that Pylaia is the most accurate model for our data. Finally, we present the preliminary findings and indicate future directions for developing a Cahuilla corpus.

Towards a Community-accessible Cahuilla corpus: Developing HTR for J.P. Harrington's handwritten fieldnotes on Mountain Cahuilla

In this paper, we aim to answer the following question: could corpus duplication be useful in Natural Language Processing (NLP) for low-resource languages? In these languages (or pi-languages), corpora available for training Large Language Models are virtually non-existent. Specifically, we study the impact of corpus expansion in Nahuatl, an agglutinative and polysynthetic Amerindian pi-language characterised by extensive dialectal variation. Our goal is to increase the size of Nahuatl corpora, which currently consist of a limited number of tokens, through controlled duplication techniques. Our experimental setup employs incremental duplication alongside appropriate corpus balancing, with the objective of training embeddings optimised for downstream NLP tasks. Consequently, static embeddings were trained and evaluated on a sentence-level semantic similarity task. Our results show a significant improvement in performance when incremental duplication is applied, compared to results obtained without corpus expansion. To our knowledge, this technique has not yet been explored in this field.

Corpora duplication for NLP in low-resource languages: A case study of Nahuatl

We present a morphosyntactically controlled transformation dataset for Quechua Collao and evaluate large language models on a sentence-level transformation task under varying prompting conditions. Results show that performance depends on the interaction between model behavior, context size, and linguistic complexity, with smaller models benefiting more from additional examples and morphological hints providing selective gains.

On the Robustness of Morphosyntactic Transformation with Large Language Models: The Case of Quechua Collao

The preservation of under-resourced languages requires digital tools and resources shaped by and for their speakers. We present the first dedicated ASR resources for Puno Quechua (ISO 639-3: qxp): (1) the largest speech corpus for any single Quechua variety, consisting in 66 hours of recordings for scripted and spontaneous speech (including 36 hours of manually transcribed and validated data), collected via a participatory design campaign; (2) the first systematic ASR benchmark for Puno Quechua, evaluating state-of-the-art models and fine-tuning Whisper-base, wav2vec2-base, and XLS-R-300M, with and without continued pre-training (CPT); (3) an open release of all datasets and fine-tuned models.

Building Community-Centred NLP Resources for Puno Quechua

In the context of real-world language technology applications, the language or variety in which a given text is written is often unknown or uncertain. Yet, this information is crucial in order to adequately select and apply appropriate models or resources. Language identification (LID), or the process of determining the language or variety of a text sample, is thus often an important fundamental task in natural language processing. LID can be particularly challenging when: (1) there are not many labeled texts for training; and (2) similar or related languages are involved, since these may share a number of surface-level features. In this paper, we present an LID system for Nahuatl, a group of closely-related language varieties spoken in Mexico and Central America. Nahuatl LID involves both of the aforementioned challenges: Nahuatl varieties can be quite similar, sharing morphemes and even many lexical items, and there is a relative paucity of representative, variant-labeled Nahuatl text. We describe LID experiments for a total of 11 Nahuatl varieties, achieving generally good results (90.59% ±0.09% in 5-fold cross-validation experiments). Many of the outstanding errors are the result of confusion between three highly similar Huasteca variants.

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