通过逐层剪枝提升翻译模型推理效率，保持翻译质量。
Iterative Layer Pruning for Efficient Translation Inference
- 基于层重要性分析，逐步剪除低效网络层。
- 模型尺寸与推理时间显著降低，翻译质量基本不变。
- 适合需要高效部署的大模型翻译系统开发者。
大规模语言模型（LLMs）已深刻改变自然语言处理领域，包括机器翻译。然而，由于计算资源需求巨大，其高效部署仍面临挑战。本文针对该问题，提交了参加2025年机器翻译会议（WMT 2025）模型压缩赛道的方案。我们通过层重要性分析指导的迭代层剪枝方法，在Aya-Expanse-8B模型上评估了从捷克语到德语、英语到埃及阿拉伯语的翻译任务。实验表明，该方法在大幅减少模型规模和推理时间的同时，维持了基线模型的翻译质量。
原文摘要 · Abstract (English)
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.
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