title: Beyond Decoder-only: Large Language Models Can be Good Encoders for
    Machine Translation
summary: The field of neural machine translation (NMT) has changed with the advent of
  large language models (LLMs). Much of the recent emphasis in natural language
  processing (NLP) has been on modeling machine translation and many other
  problems using a single pre-trained Transformer decoder, while encoder-decoder
  architectures, which were the standard in earlier NMT models, have received
  relatively less attention. In this paper, we explore translation models that
  are universal, efficient, and easy to optimize, by marrying the world of LLMs
  with the world of NMT. We apply LLMs to NMT encoding and leave the NMT decoder
  unchanged. We also develop methods for adapting LLMs to work better with the
  NMT decoder. Furthermore, we construct a new dataset involving multiple tasks
  to assess how well the machine translation system generalizes across various
  tasks. Evaluations on the WMT and our datasets show that results using our
  method match or surpass a range of baselines in terms of translation quality,
  but achieve 2.4 sim 6.5 times inference speedups and a 75% reduction in
  the memory footprint of the KV cache. It also demonstrates strong
  generalization across a variety of translation-related tasks.
paper_authors._id: 67cfd77ff8ee57c14450221b
paper_authors.name: Yingfeng Luo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-12T08:42:33.649Z
paper_authors._id: 6440b38d3e0374802e1acc5e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6440b38d3e0374802e1acc5e/w-ZpW_9gCSHUeDKyGSeMt.jpeg
paper_authors.fullname: luoyingfeng
paper_authors.type: user
paper_authors.user: luoyingfeng
paper_authors._id: 67cfd77ff8ee57c14450221c
paper_authors.name: Tong Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-12T20:46:10.813Z
paper_authors._id: 6623ea65b642e29cdf90a1b4
paper_authors.avatarUrl: /avatars/e32e90574c1162b2be87ed78604e3e4d.svg
paper_authors.fullname: TongZheng
paper_authors.type: user
paper_authors.user: TongZheng1999
paper_authors._id: 67cfd77ff8ee57c14450221d
paper_authors.name: Yongyu Mu
paper_authors._id: 67cfd77ff8ee57c14450221e
paper_authors.name: Bei Li
paper_authors._id: 67cfd77ff8ee57c14450221f
paper_authors.name: Qinghong Zhang
paper_authors._id: 67cfd77ff8ee57c144502220
paper_authors.name: Yongqi Gao
paper_authors._id: 67cfd77ff8ee57c144502221
paper_authors.name: Ziqiang Xu
paper_authors._id: 67cfd77ff8ee57c144502222
paper_authors.name: Peinan Feng
paper_authors._id: 67cfd77ff8ee57c144502223
paper_authors.name: Xiaoqian Liu
paper_authors._id: 67cfd77ff8ee57c144502224
paper_authors.name: Tong Xiao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T08:45:00.260Z
paper_authors._id: 67c5249a64910c03522af447
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/67c5249a64910c03522af447/A5h3SWp5AcamJkH150nPF.png
paper_authors.fullname: Tong Xiao
paper_authors.type: user
paper_authors.user: neupupil
paper_authors._id: 67cfd77ff8ee57c144502225
paper_authors.name: Jingbo Zhu
paper_title: Beyond Decoder-only: Large Language Models Can be Good Encoders for
    Machine Translation
paper_summary: The field of neural machine translation (NMT) has changed with the advent of
  large language models (LLMs). Much of the recent emphasis in natural language
  processing (NLP) has been on modeling machine translation and many other
  problems using a single pre-trained Transformer decoder, while encoder-decoder
  architectures, which were the standard in earlier NMT models, have received
  relatively less attention. In this paper, we explore translation models that
  are universal, efficient, and easy to optimize, by marrying the world of LLMs
  with the world of NMT. We apply LLMs to NMT encoding and leave the NMT decoder
  unchanged. We also develop methods for adapting LLMs to work better with the
  NMT decoder. Furthermore, we construct a new dataset involving multiple tasks
  to assess how well the machine translation system generalizes across various
  tasks. Evaluations on the WMT and our datasets show that results using our
  method match or surpass a range of baselines in terms of translation quality,
  but achieve 2.4 sim 6.5 times inference speedups and a 75% reduction in
  the memory footprint of the KV cache. It also demonstrates strong
  generalization across a variety of translation-related tasks.
paper_ai_summary: Combining large language models with the decoder of neural machine translation models results in improved inference speed, reduced memory usage, and strong generalization across various translation tasks.
paper_ai_keywords: neural machine translation large language models Transformer encoder-decoder inference speedup memory footprint KV cache generalization