13 citations · 13 across the 5 of their papers we have counts for
5 papers
Language on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilinguality
Mengyu Bu, Yang Feng
Large language models (LLMs) exhibit strong general intelligence, yet their multilingual performance remains highly imbalanced. Although LLMs encode substantial cross-lingual knowl…
AlignX: Advancing Multilingual Large Language Models with Multilingual Representation Alignment
Mengyu Bu, Shaolei Zhang, Zhongjun He +2
Multilingual large language models (LLMs) possess impressive multilingual understanding and generation capabilities. However, their performance and cross-lingual alignment often la…
MoCE: Adaptive Mixture of Contextualization Experts for Byte-based Neural Machine Translation
Langlin Huang, Mengyu Bu, Yang Feng
Byte-based machine translation systems have shown significant potential in massively multilingual settings. Unicode encoding, which maps each character to specific byte(s), elimina…
Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features
Mengyu Bu, Shuhao Gu, Yang Feng
The many-to-many multilingual neural machine translation can be regarded as the process of integrating semantic features from the source sentences and linguistic features from the…
BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models
Shaolei Zhang, Qingkai Fang, Zhuocheng Zhang +8
Large language models (LLMs) have demonstrated remarkable prowess in language understanding and generation. Advancing from foundation LLMs to instructionfollowing LLMs, instruction…