# Prompt and circumstance: A word-by-word LLM prompting approach to interlinear glossing for low-resource languages

arXiv: https://arxiv.org/abs/2502.09778 · HTML: https://mlsanity.com/paper/2502.09778/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Teaching Large Language Models an Unseen Language on the Fly](https://mlsanity.com/paper/2402.19167/) (2024)
- [Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?](https://mlsanity.com/paper/2409.19151/) (2024)
- [Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem](https://mlsanity.com/paper/2406.15625/) (2024)
- [Can we teach language models to gloss endangered languages?](https://mlsanity.com/paper/2406.18895/) (2024)
- [GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text](https://mlsanity.com/paper/2403.06399/) (2024)
- [Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource](https://mlsanity.com/paper/1806.03757/) (2018)
- [Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing](https://mlsanity.com/paper/2406.11085/) (2024)
- [SIGMORPHON 2023 Shared Task of Interlinear Glossing: Baseline Model](https://mlsanity.com/paper/2303.14234/) (2023)
- [Embedded Translations for Low-resource Automated Glossing](https://mlsanity.com/paper/2403.08189/) (2024)
- [From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive Grammars](https://mlsanity.com/paper/2411.15577/) (2024)

## Built on by (most cited first, 1 of 1)

- [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)

Partly automated creation of interlinear glossed text (IGT) has the potential to assist in linguistic documentation. We argue that LLMs can make this process more accessible to linguists because of their capacity to follow natural-language instructions. We investigate the effectiveness of a retrieval-based LLM prompting approach to glossing, applied to the seven languages from the SIGMORPHON 2023 shared task. Our system beats the BERT-based shared task baseline for every language in the morpheme-level score category, and we show that a simple 3-best oracle has higher word-level scores than the challenge winner (a tuned sequence model) in five languages. In a case study on Tsez, we ask the LLM to automatically create and follow linguistic instructions, reducing errors on a confusing grammatical feature. Our results thus demonstrate the potential contributions which LLMs can make in interactive systems for glossing, both in making suggestions to human annotators and following directions.