Summary of Pmmt: Preference Alignment in Multilingual Machine Translation Via Llm Distillation, by Shuqiao Sun et al.
PMMT: Preference Alignment in Multilingual Machine Translation via LLM Distillation
by Shuqiao Sun, Yutong Yao, Peiwen Wu, Feijun Jiang, Kaifu Zhang
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new approach to generate large-scale multilingual parallel corpora with specific translation preferences using Large Language Models (LLMs), bridging the gap between machine-translated texts and human- preferred tones or styles. The authors design an automatic pipeline to distill human preferences into smaller Machine Translation (MT) models, enabling efficient and cost-effective support for large-scale applications in online services. Experimental results demonstrate that this method excels in translation tasks with aligned human preferences by a significant margin, while also showcasing competitive performance on popular public benchmarks like WMT and Flores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us talk to people who speak different languages more accurately. Right now, computers can translate text, but it’s not always what humans would say. The authors created a new way to make computers generate lots of parallel texts in many languages with specific tones or styles that people prefer. They also built a system to teach smaller computer models to understand these preferences, making it easier and cheaper for big services online. Tests show that their method is really good at translating text with the right tone, and it even does well on other important benchmark tests. |
Keywords
» Artificial intelligence » Translation
