Summary of Tear: Improving Llm-based Machine Translation with Systematic Self-refinement, by Zhaopeng Feng et al.
TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement
by Zhaopeng Feng, Yan Zhang, Hao Li, Bei Wu, Jiayu Liao, Wenqiang Liu, Jun Lang, Yang Feng, Jian Wu, Zuozhu Liu
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces a new framework, TEaR (Translate, Estimate, and Refine), which utilizes Large Language Models (LLMs) to improve Machine Translation (MT) performance. By feeding back error information into LLMs, the authors demonstrate that self-refinement can lead to improved translation quality across various languages, including low-resource ones. The framework exhibits superior systematicity and interpretability compared to traditional neural translation models. Additionally, the paper explores the potential relationship between the translation and evaluation capabilities of general-purpose LLMs through cross-model correction experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using big language models to improve machine translation. These models can get better at translating languages when they learn from their mistakes. The researchers developed a new way to do this, called TEaR, which helps the models correct their errors and become better translators. They found that this approach works well for many languages, including those with limited information available. This is important because it means that we can get more accurate translations in different languages. |
Keywords
» Artificial intelligence » Translation
