Related papers: PMMT: Preference Alignment in Multilingual Machine Translation via LLM Distillation

PMMT: Preference Alignment in Multilingual Machine Translation via LLM Distillation

URL: http://arxiv.org/abs/2410.11410v1
Date: Tue, 15 Oct 2024 08:54:27 GMT
Title: PMMT: Preference Alignment in Multilingual Machine Translation via LLM Distillation
Authors: Shuqiao Sun, Yutong Yao, Peiwen Wu, Feijun Jiang, Kaifu Zhang,
Abstract summary: A new method is proposed to generate large-scale multilingual parallel corpora with specific translation preferences. Experiments indicate that the proposed method takes the lead in translation tasks with aligned human preferences by a large margin.
Score: 4.667901787486126
License:
Abstract: Translation is important for cross-language communication, and many efforts have been made to improve its accuracy. However, less investment is conducted in aligning translations with human preferences, such as translation tones or styles. In this paper, a new method is proposed to effectively generate large-scale multilingual parallel corpora with specific translation preferences using Large Language Models (LLMs). Meanwhile, an automatic pipeline is designed to distill human preferences into smaller Machine Translation (MT) models for efficiently and economically supporting large-scale calls in online services. Experiments indicate that the proposed method takes the lead in translation tasks with aligned human preferences by a large margin. Meanwhile, on popular public benchmarks like WMT and Flores, on which our models were not trained, the proposed method also shows a competitive performance compared to SOTA works.

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