55 citations · 70 across the 11 of their papers we have counts for
8 papers · 1 filter
Extrapolating Large Language Models to Non-English by Aligning Languages
Wenhao Zhu, Yunzhe Lv, Qingxiu Dong +6
Existing large language models show disparate capability across different languages, due to the imbalance in the training data. Their performances on English tasks are often strong…
SIFTER: A Task-specific Alignment Strategy for Enhancing Sentence Embeddings
Chao Yu, Wenhao Zhu, Chaoming Liu +2
The paradigm of pre-training followed by fine-tuning on downstream tasks has become the mainstream method in natural language processing tasks. Although pre-trained models have the…
INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation
Wenhao Zhu, Jingjing Xu, Shujian Huang +2
Neural machine translation has achieved promising results on many translation tasks. However, previous studies have shown that neural models induce a non-smooth representation spac…
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis
Wenhao Zhu, Hongyi Liu, Qingxiu Dong +5
Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT). In this paper, we systematically investigate the advantages…
kNN-BOX: A Unified Framework for Nearest Neighbor Generation
Wenhao Zhu, Qianfeng Zhao, Yunzhe Lv +4
Augmenting the base neural model with a token-level symbolic datastore is a novel generation paradigm and has achieved promising results in machine translation (MT). In this paper,…
What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation
Wenhao Zhu, Shujian Huang, Yunzhe Lv +2
kNN-MT presents a new paradigm for domain adaptation by building an external datastore, which usually saves all target language token occurrences in the parallel corpus. As a resul…
Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation
Fei Yuan, Yinquan Lu, WenHao Zhu +4
Multilingual neural machine translation (MNMT) aims to build a unified model for many language directions. Existing monolithic models for MNMT encounter two challenges: parameter i…
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
Wenhao Zhu, Shujian Huang, Tong Pu +6
Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which i…