Plan2Align: Predictive Planning Based Test-Time Preference Alignment in Paragraph-Level Machine Translation

• URL: http://arxiv.org/abs/2502.20795v1
• Date: Fri, 28 Feb 2025 07:24:33 GMT
• Title: Plan2Align: Predictive Planning Based Test-Time Preference Alignment in Paragraph-Level Machine Translation
• Authors: Kuang-Da Wang, Teng-Ruei Chen, Yu Heng Hung, Shuoyang Ding, Yueh-Hua Wu, Yu-Chiang Frank Wang, Chao-Han Huck Yang, Wen-Chih Peng, Ping-Chun Hsieh,
• Abstract summary: We introduce Plan2Align, a test-time alignment framework that treats translation as a predictive planning problem.<n>Plan2Align significantly improves paragraph-level translation, achieving performance surpassing or on par with the existing training-time and test-time alignment methods.
• Score: 42.89806150031301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine Translation (MT) has been predominantly designed for sentence-level translation using transformer-based architectures. While next-token prediction based Large Language Models (LLMs) demonstrate strong capabilities in long-text translation, non-extensive language models often suffer from omissions and semantic inconsistencies when processing paragraphs. Existing preference alignment methods improve sentence-level translation but fail to ensure coherence over extended contexts due to the myopic nature of next-token generation. We introduce Plan2Align, a test-time alignment framework that treats translation as a predictive planning problem, adapting Model Predictive Control to iteratively refine translation outputs. Experiments on WMT24 Discourse-Level Literary Translation show that Plan2Align significantly improves paragraph-level translation, achieving performance surpassing or on par with the existing training-time and test-time alignment methods on LLaMA-3.1 8B.

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