5 citations · 6 across the 6 of their papers we have counts for
6 papers · 1 filter
Qwen-MT-Lumen at WMT2026: Instruction-Specialized Translation via Quality-Aware Training and On-Policy Distillation
Hao Wang, Yangyang Liu, Xiaohu Zhao +11
We present Lumen, The open-source model is available at, our submission to the WMT 2026 General Machine Translation Shared Task. We focus on instruction-specialized translation, wh…
STAR : Sentence Translation Alignment Rate for Document-to-Document Machine Translation
Yichen Dong, Hao Wang, Junhui Li +3
Large Language Models (LLMs) have enabled a shift from sentence-level to document-to-document (Doc2Doc) machine translation, promising improved global coherence. However, document-…
: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation
Hao Wang, Linlong Xu, Heng Liu +11
Aligning Large Language Models (LLMs) with human preferences is pivotal for Machine Translation (MT), yet current approaches are often hindered by misleading reward signals. Our an…
TransBench: Benchmarking Machine Translation for Industrial-Scale Applications
Haijun Li, Tianqi Shi, Zifu Shang +13
Machine translation (MT) has become indispensable for cross-border communication in globalized industries like e-commerce, finance, and legal services, with recent advancements in…
Marco-LLM: Bridging Languages via Massive Multilingual Training for Cross-Lingual Enhancement
Lingfeng Ming, Bo Zeng, Chenyang Lyu +17
Large Language Models (LLMs) have achieved remarkable progress in recent years; however, their excellent performance is still largely limited to major world languages, primarily En…
Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
Yu Zhao, Huifeng Yin, Bo Zeng +6
Currently OpenAI o1 sparks a surge of interest in the study of large reasoning models (LRM). Building on this momentum, Marco-o1 not only focuses on disciplines with standard answe…