mlsanityData to 14 Oct 2026

Retrieval Augmented Spelling Correction for E-Commerce Applications

arXiv 2410.11655 ↗First posted 15 Oct 2024cs.CL cs.AIcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 14 of its 19 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

14 of 19 references.

References · 14

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  3. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  4. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
  5. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  6. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  7. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  8. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 262
  9. 2022PLAID: An Efficient Engine for Late Interaction Retrievalcited by 101
  10. 2020Relevance-guided Supervision for OpenQA with ColBERTcited by 71
  11. 2023Retrieval-Pretrained Transformer: Long-range Language Modeling with Self-retrievalcited by 20
  12. 2022Inducing Character-level Structure in Subword-based Language Models with Type-level Interchange Intervention Trainingcited by 16
2 more
  1. 2023Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentationcited by 9
  2. 2023Multi-teacher Distillation for Multilingual Spelling Correctioncited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025LMSpell: Spell Correction with Pre-Trained Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Enhancing E-commerce Product Title Translation with Retrieval-Augmented Generation and Large Language Models
  2. 2025Generative Retrieval and Alignment Model: A New Paradigm for E-commerce Retrieval
  3. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  4. 2026LLM unbranding: Erasing Commercial Identity while Preserving Generic Utility
  5. 2025CiteFix: Enhancing RAG Accuracy Through Post-Processing Citation Correction
  6. 2025RAIR: Retrieval-Augmented Iterative Refinement for Chinese Spelling Correction
  7. 2025Enhancing Retrieval-Augmented Generation: A Study of Best Practices
  8. 2025Automated Query-Product Relevance Labeling using Large Language Models for E-commerce Search