2 citations · 5 across the 7 of their papers we have counts for
7 papers
TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts
Jiangyang He, Shaolin Zhu, Deyi Xiong
Mixture-of-Experts large language models (LLMs) scale efficiently through sparse activation, yet their deployment is fundamentally constrained by the large static parameter footpri…
FuxiMT: Sparsifying Large Language Models for Chinese-Centric Multilingual Machine Translation
Shaolin Zhu, Tianyu Dong, Bo Li +1
In this paper, we present FuxiMT, a novel Chinese-centric multilingual machine translation model powered by a sparsified large language model (LLM). We adopt a two-stage strategy t…
Multilingual Large Language Models: A Systematic Survey
Shaolin Zhu, Supryadi, Shaoyang Xu +7
This paper provides a comprehensive survey of the latest research on multilingual large language models (MLLMs). MLLMs not only are able to understand and generate language across…
LANDeRMT: Detecting and Routing Language-Aware Neurons for Selectively Finetuning LLMs to Machine Translation
Shaolin Zhu, Leiyu Pan, Bo Li +1
Recent advancements in large language models (LLMs) have shown promising results in multilingual translation even with limited bilingual supervision. The major challenges are catas…
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting
Tianhao Li, Shangjie Li, Binbin Xie +2
The advent of large language models (LLMs) has predominantly catered to high-resource languages, leaving a disparity in performance for low-resource languages. Conventional Continu…
Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning
Menglong Cui, Jiangcun Du, Shaolin Zhu +1
Large language models (LLMs) exhibit outstanding performance in machine translation via in-context learning. In contrast to sentence-level translation, document-level translation (…
An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation
Supryadi, Leiyu Pan, Deyi Xiong
Massively multilingual neural machine translation (MMNMT) has been proven to enhance the translation quality of low-resource languages. In this paper, we empirically investigate th…