- We employ the Monte Carlo Tree Search (MCTS) method to generate token-level translation preference pairs (Prefixed data) for both model training and testing purposes. For contrast, we also utilize conventional approaches to generate sequence-level translation preference pairs (Arbitrary data). We partition both datasets into training and testing subsets for further evaluation.
- To assess the effectiveness of our custom dataset, we use prefixed data and Arbitrary data to train our Implicit Process Reward Model. You can reach our models in Hugging Face.
- Finally, we deploy our Implicit Process Reward Model in both Test-time Alignment and Hypothesis Ensembling frameworks, demonstrating significant performance improvements across evaluation metrics.
- We also provide the code for generating token-level prefixed translation preference pairs and utilizing our Process RM to obtain sequence-level results. You can access these codes in the code directory.
@article{feng2025mtrewardtree,
title={MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling},
author={Feng, Zhaopeng and Ren, Jiahan and Su, Jiayuan and Zheng, Jiamei and Tang, Zhihang and Wang, Hongwei and Liu, Zuozhu},
journal={arXiv preprint arXiv:2503.12123},
year={2025}
}