Summary of Llms-in-the-loop Part-1: Expert Small Ai Models For Bio-medical Text Translation, by Bunyamin Keles et al.
LLMs-in-the-loop Part-1: Expert Small AI Models for Bio-Medical Text Translation
by Bunyamin Keles, Murat Gunay, Serdar I.Caglar
First submitted to arxiv on: 16 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 This paper presents a novel approach to developing supervised neural machine translation models optimized for medical texts. The “LLMs-in-the-loop” method leverages large language models (LLMs) to improve the translation quality and accuracy of complex medical terminology, which is crucial for enabling global dissemination of medical knowledge across languages. By training small, specialized models on high-quality in-domain data, the authors show that these models can outperform even larger LLMs on tasks such as neural machine translation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to translate medical texts from one language to another. This is important because doctors and researchers need to share information with people who speak different languages. The problem is that medical words have special meanings, so machines can struggle to translate them correctly. To solve this, the authors created a new way of using big models called “LLMs” to help smaller models learn how to translate medical texts accurately. |
Keywords
» Artificial intelligence » Supervised » Translation
