Diversity-Aware Literary Machine Translation with Multi-Reward Policy Optimization

Lexical diversity and translation literalness in machine translation: a comparative study of pretrained, fine-tuned, and few-shot approaches

This paper presents a comparative study of neural machine translation (MT) models and large language models, emphasizing translation literalness, lexical diversity, and translation quality. We evaluate open-source neural machine translation (NMT) models, LLaMa 3.1 Instruct, and GPT-4o across nine language pairs in pr...

Embedding-based adaptive rejection preference optimization for low-resource machine translation

Linguistic Knowledge-Infused Fine-Tuning for Mitigating Gender Bias in Machine Translation

Large Language Models (LLMs) achieve strong performance in machine translation (MT) but often encode gender bias, particularly when translating from non-gendered into gendered languages. This paper introduces a fine-tuning strategy to mitigate such bias in English → Spanish and English → Catalan translation. Using para...

Generating Adversarial Texts for Machine Translation via GRPO

This work proposes a scalable reinforcement-learning-based approach for rewriting existing source texts into instances that are more difficult to translate for MT systems, using a large language model with Group Relative Policy Optimization (GRPO) and fine-tune a large language model with Group Relative Policy Optimiza...

Qwen-MT-Lumen at WMT2026: Instruction-Specialized Translation via Quality-Aware Training and On-Policy Distillation

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, where systems must produce accurate translations while satisfying fine-grained requirements on style, terminology, and structured output. Lume...

Flexico: Machine Translation Sustainability via Self-Adaptation