# An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages

arXiv: https://arxiv.org/abs/2604.02596 · HTML: https://mlsanity.com/paper/2604.02596/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [In-context Examples Selection for Machine Translation](https://mlsanity.com/paper/2212.02437/) (2022)
- [Finding Support Examples for In-Context Learning](https://mlsanity.com/paper/2302.13539/) (2023)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [A Benchmark for Learning to Translate a New Language from One Grammar Book](https://mlsanity.com/paper/2309.16575/) (2023)
- [Dictionary-based Phrase-level Prompting of Large Language Models for Machine Translation](https://mlsanity.com/paper/2302.07856/) (2023)
- [Teaching Large Language Models an Unseen Language on the Fly](https://mlsanity.com/paper/2402.19167/) (2024)
- [In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation](https://mlsanity.com/paper/2408.00397/) (2024)
- [Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem](https://mlsanity.com/paper/2406.15625/) (2024)
- [How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizes](https://mlsanity.com/paper/2409.03454/) (2024)
- [Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu](https://mlsanity.com/paper/2502.11862/) (2025)
- [Ibom NLP: A Step Toward Inclusive Natural Language Processing for Nigeria's Minority Languages](https://mlsanity.com/paper/2511.06531/) (2025)
- [Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation](https://mlsanity.com/paper/2602.04764/) (2026)

## Abstract (arXiv)

In-context learning (ICL) allows large language models (LLMs) to adapt to new tasks from a few examples, making it promising for languages underrepresented in pre-training. Recent work on many-shot ICL suggests that modern LLMs can further benefit from larger ICL examples enabled by their long context windows. However, such gains depend on careful example selection, and the inference cost can be prohibitive for low-resource language communities. In this paper, we present an empirical study of many-shot ICL for machine translation from English into ten truly low-resource languages recently added to FLORES+. We analyze the effects of retrieving more informative examples, using out-of-domain data, and ordering examples by length. Our findings show that many-shot ICL becomes more effective as the number of examples increases. More importantly, we show that BM25-based retrieval substantially improves data efficiency: 50 retrieved examples roughly match 250 many-shot examples, while 250 retrieved examples perform similarly to 1,000 many-shot examples. We further show that ICL provides additional gains on top of fine-tuning.