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        <title>Iterative Layer Pruning for Efficient Translation Inference</title>
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            <title>Proceedings of the Tenth Conference on Machine Translation</title>
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    <abstract>Large language models (LLMs) have transformed many areas of natural language processing, including machine translation. However, efficient deployment of LLMs remains challenging due to their intensive computational requirements. In this paper, we address this challenge and present our submissions to the Model Compression track at the Conference on Machine Translation (WMT 2025). In our experiments, we investigate iterative layer pruning guided by layer importance analysis. We evaluate this method using the Aya-Expanse-8B model for translation from Czech to German, and from English to Egyptian Arabic. Our approach achieves substantial reductions in model size and inference time, while maintaining the translation quality of the baseline models.</abstract>
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%0 Conference Proceedings
%T Iterative Layer Pruning for Efficient Translation Inference
%A Moslem, Yasmin
%A Al Farouq, Muhammad Hazim
%A Kelleher, John
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Tenth Conference on Machine Translation
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-341-8
%F moslem-etal-2025-iterative
%X Large language models (LLMs) have transformed many areas of natural language processing, including machine translation. However, efficient deployment of LLMs remains challenging due to their intensive computational requirements. In this paper, we address this challenge and present our submissions to the Model Compression track at the Conference on Machine Translation (WMT 2025). In our experiments, we investigate iterative layer pruning guided by layer importance analysis. We evaluate this method using the Aya-Expanse-8B model for translation from Czech to German, and from English to Egyptian Arabic. Our approach achieves substantial reductions in model size and inference time, while maintaining the translation quality of the baseline models.
%U https://aclanthology.org/2025.wmt-1.78/
%P 1022-1027

Markdown (Informal)

[Iterative Layer Pruning for Efficient Translation Inference](https://aclanthology.org/2025.wmt-1.78/) (Moslem et al., WMT 2025)

• Iterative Layer Pruning for Efficient Translation Inference (Moslem et al., WMT 2025)

ACL

• Yasmin Moslem, Muhammad Hazim Al Farouq, and John Kelleher. 2025. Iterative Layer Pruning for Efficient Translation Inference. In Proceedings of the Tenth Conference on Machine Translation, pages 1022–1027, Suzhou, China. Association for Computational Linguistics.