mlsanityData to 14 Oct 2026

Iterative Layer Pruning for Efficient Translation Inference

arXiv 2510.22763 ↗First posted 26 Oct 2025cs.CL cs.PFcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 26 Oct 2025.

Builds on

12 of 20 references.

References · 12

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  3. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  4. 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
  5. 2016Bag of Tricks for Efficient Text Classificationcited by 876
  6. 2019Multilingual Universal Sentence Encoder for Semantic Retrievalcited by 193
  7. 2020On the Effect of Dropping Layers of Pre-trained Transformer Modelscited by 118
  8. 2023Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labellingcited by 90
  9. 2024LLM Pruning and Distillation in Practice: The Minitron Approachcited by 82
  10. 2022Efficient Methods for Natural Language Processing: A Surveycited by 78
  11. 2025Efficient Speech Translation through Model Compression and Knowledge Distillationcited by 5
  12. 2024CULL-MT: Compression Using Language and Layer pruning for Machine Translationcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Extracting Small Translation Specialists from LLMs by Aggressively Pruning Expertscited by 3
  2. 2026AfriNLLB: Efficient Translation Models for African Languagescited by 2
  3. 2026Efficient Multilingual Neural Machine Translation via Corpus-Driven Vocabulary Pruning: An English-Arabic Case Studycited by 0
  4. 2026ESTS at WMT26: Routing-Informed Expert Pruning for Model Compressioncited by 0