Summary of Enhancing E-commerce Product Title Translation with Retrieval-augmented Generation and Large Language Models, by Bryan Zhang et al.
Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models
by Bryan Zhang, Taichi Nakatani, Stephan Walter
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Medium Difficulty Summary: A recent study proposes a novel approach to enhance multilingual large language models (LLMs) in translating e-commerce product titles cross-lingually. The Retrieval-Augmented Generation (RAG) method leverages existing bilingual product information by retrieving similar examples and using them as prompts to improve LLM-based translation quality. Experimental results demonstrate that RAG improves translation accuracy, with chrF score gains of up to 15.3% for language pairs where the LLM has limited proficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Researchers developed a new way to help machines translate product names from one language to another. This is important because many online stores sell products in different languages and need accurate translations. The new approach uses a combination of machine learning models and existing examples of bilingual product information to improve translation quality. The results show that this method can significantly improve the accuracy of product name translations, which is helpful for e-commerce. |
Keywords
» Artificial intelligence » Machine learning » Rag » Retrieval augmented generation » Translation
