Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models
This addresses the need for accurate multilingual product discovery in e-commerce, though it is incremental as it builds on existing LLM and RAG methods.
The study tackled the problem of translating e-commerce product titles by proposing a retrieval-augmented generation approach that uses bilingual examples to enhance large language model-based translation, resulting in chrF score gains of up to 15.3% for language pairs with limited LLM proficiency.
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.