---
title: "Beyond Many-Shot Translation — paper by Salim et al. (2026)"
description: "The authors study Javanese and Sundanese with two models that support 1M-token contexts… 41 references, cited by 2 papers here."
authors: ["Luis Frentzen Salim", "Esteban Carlin", "Alexandre Morinvil", "Xi Ai", "Lun-Wei Ku"]
year: 2026
venue: "Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)"
source: "https://aclanthology.org/2026.loreslm-1.34/"
doi: "10.18653/v1/2026.loreslm-1.34"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/salim-2026-beyond-many-shot-translation-scaling/"
---
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# Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation

**Authors:** [Luis Frentzen Salim](https://md.research.elamur.ai/free/en/authors/luis-frentzen-salim/), [Esteban Carlin](https://md.research.elamur.ai/free/en/authors/esteban-carlin/), Alexandre Morinvil, Xi Ai and [Lun-Wei Ku](https://md.research.elamur.ai/free/en/authors/lun-wei-ku/) · **Year:** 2026 · **Venue:** [Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)](https://md.research.elamur.ai/free/en/venues/loreslm/) ([Workshop on Language Models for Low-Resource Languages 2026](https://md.research.elamur.ai/free/en/venues/loreslm/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.loreslm-1.34/) · [PDF](https://aclanthology.org/2026.loreslm-1.34.pdf) · [DOI 10.18653/v1/2026.loreslm-1.34](https://doi.org/10.18653/v1/2026.loreslm-1.34) · [OpenAlex](https://openalex.org/W7140093916)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [2](https://md.research.elamur.ai/free/en/papers/salim-2026-beyond-many-shot-translation-scaling/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks whether long-context LLMs can be adapted to low-resource machine translation through in-context learning alone, with no fine-tuning, if the prompt holds thousands of demonstrations rather than a few. The authors study Javanese and Sundanese with two models that support 1M-token contexts: Qwen2.5-7B-Instruct-1M and Nemotron-8B-UltraLong-1M-Instruct. They vary the demonstration budget from 2^7 to 2^20 tokens and compare four sources of in-context material: crawled monolingual target-language text, Alpaca-style instruction data, and parallel corpora paired with either English or Indonesian. Quality, measured with ChrF++, rises at first, levels off around 2^14–2^16 tokens, and often falls as the prompt nears the context limit. How much it falls depends on the model and the corpus type. Parallel data usually helps most and raw monolingual text least, but instruction-style data in the target language can come close to parallel data. The practical lesson is that filling the context window is not an efficient strategy, and choosing demonstrations carefully matters more than adding more of them.

## Key points
- Models: Qwen2.5-7B-Instruct-1M and Nemotron-8B-UltraLong-1M-Instruct, run with vLLM at temperature 0.7. Both directions are tested: English↔Javanese and English↔Sundanese.
- Gains saturate at roughly 2^14–2^16 shot tokens, far below the 1M limit. Near the limit, quality can degrade and generation can become unstable, most clearly for Qwen with some corpus types.
- Monolingual demonstrations reach the lowest peak and decline earliest. Instruction and parallel corpora hold their performance up to larger budgets. The advantage of parallel over monolingual data is larger when translating from English into the target language than in the reverse direction.
- Whether Indonesian or English works better as the parallel reference language depends on the translation direction. Indonesian was included because it is typologically closer to Javanese and Sundanese.
- The authors release synthetic, manually reviewed English–Indonesian–Javanese and English–Indonesian–Sundanese parallel triplets.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Institute of Information Science, Academia Sinica](https://md.research.elamur.ai/free/en/labs/institute-of-information-science-academia-sinica/) — Research institute, Taipei, Taiwan · 7 works · 51 citations
- [Centrale Méditerranée](https://md.research.elamur.ai/free/en/universities/centrale-mediterranee/) — University, Marseille, France · 2 works · 38,336 citations
- [National Taiwan University of Science and Technology](https://md.research.elamur.ai/free/en/universities/national-taiwan-university-of-science-and-technology/) — University, Taipei, Taiwan · 4 works · 6 citations

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## Cite

Cite as: Luis Frentzen Salim, Esteban Carlin, Alexandre Morinvil, Xi Ai and Lun-Wei Ku. 2026. Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation. Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{salim2026beyond,
  title = {{Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation}},
  author = {Luis Frentzen Salim, Esteban Carlin, Alexandre Morinvil, Xi Ai and Lun-Wei Ku},
  booktitle = {Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.loreslm-1.34},
  url = {https://doi.org/10.18653/v1/2026.loreslm-1.34}
}
```

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*Updated 1 November 2026*