Summary of Llm-based Translation Inference with Iterative Bilingual Understanding, by Andong Chen et al.
LLM-based Translation Inference with Iterative Bilingual Understanding
by Andong Chen, Kehai Chen, Yang Xiang, Xuefeng Bai, Muyun Yang, Yang Feng, Tiejun Zhao, Min zhang
First submitted to arxiv on: 16 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 The novel Iterative Bilingual Understanding Translation (IBUT) method leverages large language models’ cross-lingual capabilities and dual translation characteristics to improve translation performance. By generating contextual understanding for both source and target languages, IBUT iteratively refines its understanding through cross-lingual feedback, reducing errors and enhancing translation quality. The proposed approach outperforms several strong comparison methods across various domains, including news, commonsense, and cultural benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to improve machine translation using large language models. Right now, these models can understand and generate text very well. However, sometimes they misunderstand the text being translated, which makes the results not as good. To fix this, they created a method called Iterative Bilingual Understanding Translation (IBUT). It works by understanding both the original text and its translation separately, then using that information to make corrections. This helps get better translations. The new approach did really well in tests against other methods for translating different types of texts. |
Keywords
» Artificial intelligence » Translation
