用检索增强生成提升电商拼写纠错，区分品牌名和真实拼写错误。
Retrieval Augmented Spelling Correction for E-Commerce Applications
- 通过检索商品目录增强大模型上下文进行拼写纠错。
- 相比纯大模型，拼写纠错准确率显著提升。
- 适合需要处理新品牌名的电商平台使用。
日常语言中新品牌名称的快速涌现给电商拼写纠错服务带来了独特挑战，需区分真实拼写错误与采用非标准拼写的新型品牌名。本文提出基于检索增强生成（RAG）的方法：从商品目录中检索产品名称并融入大语言模型（LLM）的上下文，该模型经过微调以执行上下文相关的拼写纠错。通过定量评估和定性错误分析，结果表明，采用RAG框架的拼写纠错效果优于独立的LLM。同时，我们还证明了对LLM进行额外微调以融合检索到的上下文具有显著价值。
原文摘要 · Abstract (English)
The rapid introduction of new brand names into everyday language poses a unique challenge for e-commerce spelling correction services, which must distinguish genuine misspellings from novel brand names that use unconventional spelling. We seek to address this challenge via Retrieval Augmented Generation (RAG). On this approach, product names are retrieved from a catalog and incorporated into the context used by a large language model (LLM) that has been fine-tuned to do contextual spelling correction. Through quantitative evaluation and qualitative error analyses, we find improvements in spelling correction utilizing the RAG framework beyond a stand-alone LLM. We also demonstrate the value of additional finetuning of the LLM to incorporate retrieved context.
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