Summary of Translate-and-revise: Boosting Large Language Models For Constrained Translation, by Pengcheng Huang and Yongyu Mu and Yuzhang Wu and Bei Li and Chunyang Xiao and Tong Xiao and Jingbo Zhu
Translate-and-Revise: Boosting Large Language Models for Constrained Translation
by Pengcheng Huang, Yongyu Mu, Yuzhang Wu, Bei Li, Chunyang Xiao, Tong Xiao, Jingbo Zhu
First submitted to arxiv on: 18 Jul 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 A new study proposes a way to improve machine translation systems by using large language models (LLMs) with constraints. Currently, these systems aren’t trained to follow rules or guidelines when generating translations, which can lead to inaccurate results. The researchers suggest adapting LLMs to take instruction prompts and constraints as input, allowing them to generate more adequate and fluent translations. To overcome potential biases in the model’s predictions, they propose adding a revision process that encourages the LLM to correct its outputs based on the remaining constraints. This approach is tested on four different constrained translation tasks and shows a 15% improvement over standard LLMs. It also outperforms state-of-the-art neural machine translation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way has been found to make computer translations more accurate by adding rules or guidelines. Right now, these systems just generate translations without following any specific rules. This can lead to mistakes in the translation. The researchers found a way to adapt big language models to follow these rules and guidelines when generating translations. To make sure the model doesn’t get biased, they added a step that encourages it to correct its work based on the remaining guidelines. They tested this approach with four different translation tasks and saw a 15% improvement over just using regular big language models. It even outperformed other top methods for translating texts. |
Keywords
» Artificial intelligence » Translation
