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20202026
most citedODE Transformer: An Ordinary Differential Equation-Inspired Model for Neural Machine Translation

11 citations · 17 across the 7 of their papers we have counts for

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9 papers · 1 filter

cs.CL2026

RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment

Yingfeng Luo, Hongyu Liu, Dingyang Lin +6

Large Language Models (LLMs) have achieved remarkable performance in Machine Translation (MT), but deploying them at scale remains prohibitively expensive. A widely adopted remedy…

cs.CL2025

NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs

Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang +9

Large language models have significantly advanced Multilingual Machine Translation (MMT), yet scaling to many languages while keeping quality robust across directions remains chall…

cs.CL2025

LaTeXTrans: Structured LaTeX Translation with Multi-Agent Coordination

Ziming Zhu, Chenglong Wang, Haosong Xv +8

Despite the remarkable progress of modern machine translation (MT) systems on general-domain texts, translating structured LaTeX-formatted documents remains a significant challenge…

cs.CL2023

Learning Evaluation Models from Large Language Models for Sequence Generation

Chenglong Wang, Hang Zhou, Kaiyan Chang +6

Automatic evaluation of sequence generation, traditionally reliant on metrics like BLEU and ROUGE, often fails to capture the semantic accuracy of generated text sequences due to t…

cs.CL20222 cited

ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation

Bei Li, Quan Du, Tao Zhou +7

Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). This paper explores a deeper relationship between Transformer and numerical ODE…

cs.CL202111 cited

ODE Transformer: An Ordinary Differential Equation-Inspired Model for Neural Machine Translation

Bei Li, Quan Du, Tao Zhou +4

It has been found that residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODEs). In this paper, we explore a deeper relationship between…