5 papers
The Astonishing Ability of Large Language Models to Parse Jabberwockified Language
Gary Lupyan, Senyi Yang
We show that large language models (LLMs) have an astonishing ability to recover meaning from severely degraded English texts. Texts in which content words have been randomly subst…
GRRM: Group Relative Reward Modeling for Machine Translation
Sen Yang, Shanbo Cheng, Lu Xu +2
While Group Relative Policy Optimization (GRPO) offers a powerful framework for LLM post-training, its effectiveness in open-ended domains like Machine Translation hinges on accura…
EnAnchored-X2X: English-Anchored Optimization for Many-to-Many Translation
Sen Yang, Yu Bao, Yu Lu +3
Large language models (LLMs) have demonstrated strong machine translation capabilities for English-centric language pairs but underperform in direct non-English (x2x) translation.…
Seed-X: Building Strong Multilingual Translation LLM with 7B Parameters
Shanbo Cheng, Yu Bao, Qian Cao +23
Multilingual translation stands as a challenging task for large language models (LLMs) to handle intricate language patterns and stilted translations that arise in automated transl…
Trans-Zero: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data
Wei Zou, Sen Yang, Yu Bao +3
The rise of Large Language Models (LLMs) has reshaped machine translation (MT), but multilingual MT still relies heavily on parallel data for supervised fine-tuning (SFT), facing c…