Summary of Retrieval Augmented Spelling Correction For E-commerce Applications, by Xuan Guo et al.
Retrieval Augmented Spelling Correction for E-Commerce Applications
by Xuan Guo, Rohit Patki, Dante Everaert, Christopher Potts
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper addresses the challenge of e-commerce spelling correction services in distinguishing genuine misspellings from novel brand names using unconventional spelling. The authors propose Retrieval Augmented Generation (RAG), a framework that retrieves product names from a catalog and incorporates them into the context used by a large language model (LLM) fine-tuned for contextual spelling correction. The RAG approach improves spelling correction performance compared to a standalone LLM, with additional fine-tuning of the LLM further enhancing results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tackles a unique problem in e-commerce spelling correction services, where they must distinguish between genuine misspellings and novel brand names using unconventional spelling. The authors propose a solution called Retrieval Augmented Generation (RAG) that uses a large language model to correct spelling errors. They show that RAG improves spelling correction performance compared to just using the language model alone. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Large language model » Rag » Retrieval augmented generation
