用相似商品标题增强翻译，提升电商标题跨语言准确性
Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models
- 从电商库中检索相似双语标题作为提示，增强大模型翻译
- 在低资源语言对上实现最高15.3%的chrF提升
- 适合需要精准电商标题翻译的开发者和平台
电商平台支持多语言商品发现，依赖准确的商品标题翻译。多语言大语言模型（LLMs）在机器翻译任务中展现出潜力，可一步完成跨语言商品标题的翻译与增强。然而，商品标题通常很短、缺乏上下文且包含专业术语，仅靠语言转换难以保证质量。本研究提出一种检索增强生成（RAG）方法，利用电商中已有的双语商品信息，通过检索相似的双语示例，并将其作为少样本提示融入模型，以提升基于LLM的商品标题翻译效果。实验结果表明，该方法在大模型能力有限的语言对上，将标题翻译质量提升了最多15.3%的chrF得分。
原文摘要 · Abstract (English)
E-commerce stores enable multilingual product discovery which require accurate product title translation. Multilingual large language models (LLMs) have shown promising capacity to perform machine translation tasks, and it can also enhance and translate product titles cross-lingually in one step. However, product title translation often requires more than just language conversion because titles are short, lack context, and contain specialized terminology. This study proposes a retrieval-augmented generation (RAG) approach that leverages existing bilingual product information in e-commerce by retrieving similar bilingual examples and incorporating them as few-shot prompts to enhance LLM-based product title translation. Experiment results show that our proposed RAG approach improve product title translation quality with chrF score gains of up to 15.3% for language pairs where the LLM has limited proficiency.
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