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Extremely low-resource (XLR) languages lack substantial corpora for trainingNLP models, motivating the use of all available resources such as dictionariesand grammar books. Machine Translation from One Book (Tanzer et al., 2024)suggests that prompting long-context LLMs with one grammar book enablesEnglish-Kalamang translation, an XLR language unseen by LLMs - a noteworthycase of linguistics helping an NLP task. We investigate the source of thistranslation ability, finding almost all improvements stem from the book'sparallel examples rather than its grammatical explanations. We find similarresults for Nepali and Guarani, seen low-resource languages, and we achieveperformance comparable to an LLM with a grammar book by simply fine-tuning anencoder-decoder translation model. We then investigate where grammar books helpby testing two linguistic tasks, grammaticality judgment and gloss prediction,and we explore what kind of grammatical knowledge helps by introducing atypological feature prompt that achieves leading results on these more relevanttasks. We thus emphasise the importance of task-appropriate data for XLRlanguages: parallel examples for translation, and grammatical data forlinguistic tasks. As we find no evidence that long-context LLMs can makeeffective use of grammatical explanations for XLR translation, we conclude datacollection for multilingual XLR tasks such as translation is best focused onparallel data over linguistic description.

Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?