15 citations · 30 across the 8 of their papers we have counts for
9 papers
PART: Progressive Alignment Representation Training for Multilingual Speech-To-Text with LLMs
Pei Zhang, Andong Chen, Xi Chen +3
Large language models (LLMs) have expanded from text to speech, giving rise to Speech Large Models (SLMs) that support recognition, translation, and synthesis. A key challenge is a…
Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decoding
Yiming Wang, Pei Zhang, Siyuan Huang +4
Test-time scaling enhances large language model performance by allocating additional compute resources during inference. Best-of-N (BoN) sampling serves as a common sampling-based…
PolyLM: An Open Source Polyglot Large Language Model
Xiangpeng Wei, Haoran Wei, Huan Lin +15
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions. However, the development of LLMs has been pr…
Optimized architectures for universal quantum state transformations using photonic path and polarization
Dong-Xu Chen, Junliang Jia, Pei Zhang +1
An arbitrary lossless transformation in high-dimensional quantum space can be decomposed into elementary operations which are easy to implement, and an effective decomposition algo…
Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?
Pei Zhang, Baosong Yang, Haoran Wei +4
Neural machine translation (NMT) is often criticized for failures that happen without awareness. The lack of competency awareness makes NMT untrustworthy. This is in sharp contrast…
Long-Short Term Masking Transformer: A Simple but Effective Baseline for Document-level Neural Machine Translation
Pei Zhang, Boxing Chen, Niyu Ge +1
Many document-level neural machine translation (NMT) systems have explored the utility of context-aware architecture, usually requiring an increasing number of parameters and compu…
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation
Pei Zhang, Xu Zhang, Wei Chen +3
Document-level machine translation incorporates inter-sentential dependencies into the translation of a source sentence. In this paper, we propose a new framework to model cross-se…
Visual Agreement Regularized Training for Multi-Modal Machine Translation
Pengcheng Yang, Boxing Chen, Pei Zhang +1
Multi-modal machine translation aims at translating the source sentence into a different language in the presence of the paired image. Previous work suggests that additional visual…
Lattice Transformer for Speech Translation
Pei Zhang, Boxing Chen, Niyu Ge +1
Recent advances in sequence modeling have highlighted the strengths of the transformer architecture, especially in achieving state-of-the-art machine translation results. However,…