@inproceedings{coleman-etal-2026-comparing,
title = "Comparing {LLM}-Based Translation Approaches for Extremely Low-Resource Languages",
author = "Coleman, Jared and
Rosales, Ruben and
Toal, Kira and
Cuadros, Diego and
Leeds, Nicholas and
Krishnamachari, Bhaskar and
Iskarous, Khalil",
editor = "Ojha, Atul Kr. and
Liu, Chao-hong and
Vylomova, Ekaterina and
Pirinen, Flammie and
Washington, Jonathan and
Oco, Nathaniel and
Zhao, Xiaobing",
booktitle = "Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages ({L}o{R}es{MT} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.loresmt-1.4/",
pages = "49--68",
ISBN = "979-8-89176-366-1",
abstract = "We present a comprehensive evaluation and extension of the LLM-Assisted Rule-Based Machine Translation (LLM-RBMT) paradigm, an approach that combines the strengths of rule-based methods and Large Language Models (LLMs) to support translation in no-resource settings. We present a robust new implementation (the Pipeline Translator) that generalizes the LLM-RBMT approach and enables flexible adaptation to novel constructions. We benchmark it against four alternatives (Builder, Instructions, RAG, and Fine-tuned translators) on a curated dataset of 150 English sentences, and compare them across translation quality and runtime. The Pipeline Translator consistently achieves the best overall performance. The LLM-RBMT methods (Pipeline and Builder) also offer an important advantage: they naturally align with evaluation strategies that prioritize grammaticality and semantic fidelity over surface-form overlap, which is critical for endangered languages where mistranslation carries high risk."
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%0 Conference Proceedings
%T Comparing LLM-Based Translation Approaches for Extremely Low-Resource Languages
%A Coleman, Jared
%A Rosales, Ruben
%A Toal, Kira
%A Cuadros, Diego
%A Leeds, Nicholas
%A Krishnamachari, Bhaskar
%A Iskarous, Khalil
%Y Ojha, Atul Kr.
%Y Liu, Chao-hong
%Y Vylomova, Ekaterina
%Y Pirinen, Flammie
%Y Washington, Jonathan
%Y Oco, Nathaniel
%Y Zhao, Xiaobing
%S Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-366-1
%F coleman-etal-2026-comparing
%X We present a comprehensive evaluation and extension of the LLM-Assisted Rule-Based Machine Translation (LLM-RBMT) paradigm, an approach that combines the strengths of rule-based methods and Large Language Models (LLMs) to support translation in no-resource settings. We present a robust new implementation (the Pipeline Translator) that generalizes the LLM-RBMT approach and enables flexible adaptation to novel constructions. We benchmark it against four alternatives (Builder, Instructions, RAG, and Fine-tuned translators) on a curated dataset of 150 English sentences, and compare them across translation quality and runtime. The Pipeline Translator consistently achieves the best overall performance. The LLM-RBMT methods (Pipeline and Builder) also offer an important advantage: they naturally align with evaluation strategies that prioritize grammaticality and semantic fidelity over surface-form overlap, which is critical for endangered languages where mistranslation carries high risk.
%U https://aclanthology.org/2026.loresmt-1.4/
%P 49-68
Markdown (Informal)
[Comparing LLM-Based Translation Approaches for Extremely Low-Resource Languages](https://aclanthology.org/2026.loresmt-1.4/) (Coleman et al., LoResMT 2026)
ACL
- Jared Coleman, Ruben Rosales, Kira Toal, Diego Cuadros, Nicholas Leeds, Bhaskar Krishnamachari, and Khalil Iskarous. 2026. Comparing LLM-Based Translation Approaches for Extremely Low-Resource Languages. In Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026), pages 49–68, Rabat, Morocco. Association for Computational Linguistics.