Beyond Decoder-only: Large Language Models Can be Good Encoders for
  Machine Translation
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.
67cfd77ff8ee57c14450221b
Yingfeng Luo
claimed_verified
2025-03-12T08:42:33.649Z
6440b38d3e0374802e1acc5e
https://cdn-avatars.huggingface.co/v1/production/uploads/6440b38d3e0374802e1acc5e/w-ZpW_9gCSHUeDKyGSeMt.jpeg
luoyingfeng
user
luoyingfeng
67cfd77ff8ee57c14450221c
Tong Zheng
claimed_verified
2025-03-12T20:46:10.813Z
6623ea65b642e29cdf90a1b4
/avatars/e32e90574c1162b2be87ed78604e3e4d.svg
TongZheng
user
TongZheng1999
67cfd77ff8ee57c14450221d
Yongyu Mu
67cfd77ff8ee57c14450221e
Bei Li
67cfd77ff8ee57c14450221f
Qinghong Zhang
67cfd77ff8ee57c144502220
Yongqi Gao
67cfd77ff8ee57c144502221
Ziqiang Xu
67cfd77ff8ee57c144502222
Peinan Feng
67cfd77ff8ee57c144502223
Xiaoqian Liu
67cfd77ff8ee57c144502224
Tong Xiao
claimed_verified
2025-03-17T08:45:00.260Z
67c5249a64910c03522af447
https://cdn-avatars.huggingface.co/v1/production/uploads/67c5249a64910c03522af447/A5h3SWp5AcamJkH150nPF.png
Tong Xiao
user
neupupil
67cfd77ff8ee57c144502225
Jingbo Zhu
Beyond Decoder-only: Large Language Models Can be Good Encoders for
  Machine Translation
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.
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.
neural machine translation large language models Transformer encoder-decoder inference speedup memory footprint KV cache generalization