# Can we teach language models to gloss endangered languages?

arXiv: https://arxiv.org/abs/2406.18895 · HTML: https://mlsanity.com/paper/2406.18895/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 62 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Few-shot Learning with Multilingual Language Models](https://mlsanity.com/paper/2112.10668/) (2021)
- [In-Context Learning with Long-Context Models: An In-Depth Exploration](https://mlsanity.com/paper/2405.00200/) (2024)
- [Language Models are Few-shot Multilingual Learners](https://mlsanity.com/paper/2109.07684/) (2021)
- [A Benchmark for Learning to Translate a New Language from One Grammar Book](https://mlsanity.com/paper/2309.16575/) (2023)
- [LLMs Are Few-Shot In-Context Low-Resource Language Learners](https://mlsanity.com/paper/2403.16512/) (2024)
- [Not always about you: Prioritizing community needs when developing endangered language technology](https://mlsanity.com/paper/2204.05541/) (2022)
- [How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language](https://mlsanity.com/paper/2204.11909/) (2022)
- [Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions](https://mlsanity.com/paper/2402.18025/) (2024)
- [Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation](https://mlsanity.com/paper/1911.02709/) (2019)
- [Morphological Processing of Low-Resource Languages: Where We Are and What's Next](https://mlsanity.com/paper/2203.08909/) (2022)
- [Robust Generalization Strategies for Morpheme Glossing in an Endangered Language Documentation Context](https://mlsanity.com/paper/2311.02777/) (2023)

## Built on by (most cited first, 14 of 14)

- [Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?](https://mlsanity.com/paper/2409.19151/) (2024)
- [LingGym: How Far Are LLMs from Thinking Like Field Linguists?](https://mlsanity.com/paper/2511.00343/) (2025)
- [Read it in Two Steps: Translating Extremely Low-Resource Languages with Code-Augmented Grammar Books](https://mlsanity.com/paper/2506.01796/) (2025)
- [ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline](https://mlsanity.com/paper/2508.06094/) (2025)
- [Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation](https://mlsanity.com/paper/2509.10644/) (2025)
- [In-context Language Learning for Endangered Languages in Speech Recognition](https://mlsanity.com/paper/2505.20445/) (2025)
- [Prompt and circumstance: A word-by-word LLM prompting approach to interlinear glossing for low-resource languages](https://mlsanity.com/paper/2502.09778/) (2025)
- [Massively Multilingual Joint Segmentation and Glossing](https://mlsanity.com/paper/2601.10925/) (2026)
- [Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.00387/) (2024)
- [Hybrid Neural-LLM Pipeline for Morphological Glossing in Endangered Language Documentation: A Case Study of Jungar Tuvan](https://mlsanity.com/paper/2603.00923/) (2026)
- [Arabic Morphosyntactic Tagging and Dependency Parsing with Large Language Models](https://mlsanity.com/paper/2603.16718/) (2026)
- [CWoMP: Morpheme Representation Learning for Interlinear Glossing](https://mlsanity.com/paper/2603.18184/) (2026)
- [GlossAssist -- A Tool to Simplify Corpus Creation and Study the Effect of NLP Models in Low-Resource Documentation Settings](https://mlsanity.com/paper/2606.04367/) (2026)
- [A POS Tier Is the Key to Automated Annotation for Low-Resource Language Documentation: Neural Interlinear Glossing for Irabu, a Southern Ryukyuan Language](https://mlsanity.com/paper/2607.13372/) (2026)

## Abstract (arXiv)

Interlinear glossed text (IGT) is a popular format in language documentation projects, where each morpheme is labeled with a descriptive annotation. Automating the creation of interlinear glossed text would be desirable to reduce annotator effort and maintain consistency across annotated corpora. Prior research has explored a number of statistical and neural methods for automatically producing IGT. As large language models (LLMs) have showed promising results across multilingual tasks, even for rare, endangered languages, it is natural to wonder whether they can be utilized for the task of generating IGT. We explore whether LLMs can be effective at the task of interlinear glossing with in-context learning, without any traditional training. We propose new approaches for selecting examples to provide in-context, observing that targeted selection can significantly improve performance. We find that LLM-based methods beat standard transformer baselines, despite requiring no training at all. These approaches still underperform state-of-the-art supervised systems for the task, but are highly practical for researchers outside of the NLP community, requiring minimal effort to use.