Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914952818720768 |
|---|---|
| author | Zhang, Bryan Nakatani, Taichi Walter, Stephan |
| author_facet | Zhang, Bryan Nakatani, Taichi Walter, Stephan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12880 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models Zhang, Bryan Nakatani, Taichi Walter, Stephan Computation and Language Artificial Intelligence Machine Learning 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. |
| title | Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.12880 |