Summary of Improving Llm Abilities in Idiomatic Translation, by Sundesh Donthi et al.
Improving LLM Abilities in Idiomatic Translation
by Sundesh Donthi, Maximilian Spencer, Om Patel, Joon Doh, Eid Rodan, Kevin Zhu, Sean O’Brien
First submitted to arxiv on: 3 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 The proposed research aims to enhance translation fidelity for large language models (LLMs) by improving their processing of idiomatic language while preserving the original linguistic style. To achieve this, the study utilizes knowledge bases like IdiomKB to provide LLMs with the meaning of an idiom and then employs two methods to translate idioms: Cosine Similarity method and LLM-generated idiom method. The research also evaluates the effectiveness of these methods on English-Chinese and Chinese-English translations using human evaluations. Furthermore, the study develops a low-resource Urdu dataset containing Urdu idioms and their translations, which shows promise in overcoming language barriers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to make computers better at translating tricky phrases called idioms. Idioms can be hard to understand because they don’t always mean what the words say. Right now, computer programs have trouble keeping the same tone and style when translating idioms from one language to another. The team is working on improving this by giving these computer programs a special knowledge base that helps them understand the meaning of idioms. They are testing two new ways to translate idioms: one uses math to find similar phrases, and the other uses a different computer program to find a good match. So far, their method has worked well in translating idioms from English to Chinese and vice versa. The team is also creating a special database for Urdu idioms, which could help people understand more literature from around the world. |
Keywords
» Artificial intelligence » Cosine similarity » Knowledge base » Translation
