5 papers
PAMT: Process-Aligned Reinforcement Learning for Multi-Domain Machine Translation
Yongshi Ye, Biao Fu, Chongxuan Huang +2
Multi-domain machine translation (MDMT) requires more than fluent generation: it demands domain-sensitive translation decisions such as domain disambiguation, terminology control,…
Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation
Yongshi Ye, Biao Fu, Chongxuan Huang +2
Multi-domain machine translation (MDMT) poses a unique challenge due to varying levels of linguistic complexity across domains. Inspired by human translators' ability to adapt reas…
DARL: Encouraging Diverse Answers for General Reasoning without Verifiers
Chongxuan Huang, Lei Lin, Xiaodong Shi +2
Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated promising gains in enhancing the reasoning capabilities of large language models. However, its dependence on…
From Neurons to Semantics: Evaluating Cross-Linguistic Alignment Capabilities of Large Language Models via Neurons Alignment
Chongxuan Huang, Yongshi Ye, Biao Fu +2
Large language models (LLMs) have demonstrated remarkable multilingual capabilities, however, how to evaluate cross-lingual alignment remains underexplored. Existing alignment benc…
How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation
Yongshi Ye, Biao Fu, Chongxuan Huang +2
Large language models (LLMs) have demonstrated strong performance in general-purpose machine translation, but their effectiveness in complex, domain-sensitive translation tasks rem…