# Back to School: Translation Using Grammar Books

arXiv: https://arxiv.org/abs/2410.15263 · HTML: https://mlsanity.com/paper/2410.15263/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Systematic Inequalities in Language Technology Performance across the World's Languages](https://mlsanity.com/paper/2110.06733/) (2021)
- [A Benchmark for Learning to Translate a New Language from One Grammar Book](https://mlsanity.com/paper/2309.16575/) (2023)
- [Building Machine Translation Systems for the Next Thousand Languages](https://mlsanity.com/paper/2205.03983/) (2022)
- [Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions](https://mlsanity.com/paper/2402.18025/) (2024)

## Built on by (most cited first, 11 of 11)

- [Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu](https://mlsanity.com/paper/2502.11862/) (2025)
- [Read it in Two Steps: Translating Extremely Low-Resource Languages with Code-Augmented Grammar Books](https://mlsanity.com/paper/2506.01796/) (2025)
- [The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in LLMs with Camlang](https://mlsanity.com/paper/2509.00425/) (2025)
- [Evaluating Extremely Low-Resource Machine Translation: A Comparative Study of ChrF++ and BLEU Metrics](https://mlsanity.com/paper/2602.17425/) (2026)
- [Speaking of Language: Reflections on Metalanguage Research in NLP](https://mlsanity.com/paper/2604.02645/) (2026)
- [Testing the Limits of Machine Translation from One Book](https://mlsanity.com/paper/2508.06665/) (2025)
- [Rashid: A Cipher-Based Framework for Exploring In-Context Language Learning](https://mlsanity.com/paper/2603.22497/) (2026)
- [Efficient Low-Resource Language Adaptation via Multi-Source Dynamic Logit Fusion](https://mlsanity.com/paper/2604.18106/) (2026)
- [Reinforcement Learning Elicits Contextual Learning of Unseen Language Translation](https://mlsanity.com/paper/2606.06428/) (2026)
- [A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Books](https://mlsanity.com/paper/2607.22376/) (2026)
- [LLM Agents as Computational Typologists](https://mlsanity.com/paper/2609.07791/) (2026)

## Abstract (arXiv)

Machine translation systems for high resource languages perform exceptionally well and produce high quality translations. Unfortunately, the vast majority of languages are not considered high resource and lack the quantity of parallel sentences needed to train such systems. These under-represented languages are not without resources, however, and bilingual dictionaries and grammar books are available as linguistic reference material. With current large language models (LLMs) supporting near book-length contexts, we can begin to use the available material to ensure advancements are shared among all of the world's languages. In this paper, we demonstrate incorporating grammar books in the prompt of GPT-4 to improve machine translation and evaluate the performance on 16 topologically diverse low-resource languages, using a combination of reference material to show that the machine translation performance of LLMs can be improved using this method.