Summary of Gentranslate: Large Language Models Are Generative Multilingual Speech and Machine Translators, by Yuchen Hu et al.
GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators
by Yuchen Hu, Chen Chen, Chao-Han Huck Yang, Ruizhe Li, Dong Zhang, Zhehuai Chen, Eng Siong Chng
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 The proposed “GenTranslate” generative paradigm leverages large language models to generate better results from diverse N-best translation candidates. By integrating rich linguistic knowledge and reasoning abilities, GenTranslate can produce higher-quality translations. The paper also introduces a new dataset, HypoTranslate, containing over 592K hypotheses-translation pairs in 11 languages. Experiments on various benchmarks show that GenTranslate outperforms state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to translate words from one language to another. They used special computer programs called large language models to help with this process. These models are very good at understanding and generating text in many languages. The new method, called GenTranslate, takes into account the many possible translations that these models can come up with, rather than just choosing the best one. This leads to better translation results. The researchers also created a large dataset of different translations to help train their model. They tested their approach on several language translation tasks and found it outperformed existing methods. |
Keywords
* Artificial intelligence * Translation
