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    <abstract>LLMs have been shown to perform well in machine translation (MT) with the use of in-context learning, rivalling supervised models when translating into high-resource languages (HRLs). However, they lag behind when dealing with low-resource language (LRLs). Example selection via similarity search and supervised fine-tuning help. However the improvements they give are limited by the size, quality and diversity of existing parallel datasets. A common technique in low-resource MT is synthetic parallel data creation, the most frequent of which is backtranslation, whereby existing target-side texts are automatically translated into the source language. However, it also relies on the existence of good quality and relevant target-side texts, which are not readily available for many LRLs. In this paper, we present a new approach, TopXGen, which involves using an LLM to automatically generate topic-specific target-side data in the LRL, which can then be backtranslated to produce useful and diverse parallel texts for ICL and fine-tuning. Our intuition is that while LLMs struggle to translate into LRLs, their ability to translate well into HRLs and their multilinguality enable them to generate good quality, natural-sounding target-side texts, which can be translated well into a high-resource source language. We show that TopXGen boosts LLM translation performance during fine-tuning and in-context learning. Our code and outputs will be made freely available.</abstract>
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%0 Conference Proceedings
%T TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation
%A Zebaze, Armel Randy
%A Sagot, Benoît
%A Bawden, Rachel
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zebaze-etal-2025-topxgen
%X LLMs have been shown to perform well in machine translation (MT) with the use of in-context learning, rivalling supervised models when translating into high-resource languages (HRLs). However, they lag behind when dealing with low-resource language (LRLs). Example selection via similarity search and supervised fine-tuning help. However the improvements they give are limited by the size, quality and diversity of existing parallel datasets. A common technique in low-resource MT is synthetic parallel data creation, the most frequent of which is backtranslation, whereby existing target-side texts are automatically translated into the source language. However, it also relies on the existence of good quality and relevant target-side texts, which are not readily available for many LRLs. In this paper, we present a new approach, TopXGen, which involves using an LLM to automatically generate topic-specific target-side data in the LRL, which can then be backtranslated to produce useful and diverse parallel texts for ICL and fine-tuning. Our intuition is that while LLMs struggle to translate into LRLs, their ability to translate well into HRLs and their multilinguality enable them to generate good quality, natural-sounding target-side texts, which can be translated well into a high-resource source language. We show that TopXGen boosts LLM translation performance during fine-tuning and in-context learning. Our code and outputs will be made freely available.
%R 10.18653/v1/2025.findings-emnlp.1217
%U https://aclanthology.org/2025.findings-emnlp.1217/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1217
%P 22358-22381

Markdown (Informal)

[TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation](https://aclanthology.org/2025.findings-emnlp.1217/) (Zebaze et al., Findings 2025)

• TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation (Zebaze et al., Findings 2025)

ACL

• Armel Randy Zebaze, Benoît Sagot, and Rachel Bawden. 2025. TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 22358–22381, Suzhou, China. Association for Computational Linguistics.