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cloud.google.com·6h ago
Data Science
Towards Data Science·Aug 30·5 min read
Noisy Text in RAG: Typos, OCR, and the Gap Classical Spell-Check Leaves
TL;DR
Noisy text from user typos, transcription errors, and OCR mistakes poses challenges for document intelligence. Classical spell-check only addresses typos, while embeddings help manage the rest.
✦ Why It Matters
Integrate embeddings into your text processing systems to improve accuracy in handling noisy text.
Key Takeaways
- Adopt embeddings for better handling of typos in queries.
- Utilize LLMs to confirm context and intent in retrieval.
- Recognize limitations of classical spell-check in enterprise settings.
- Focus on line-level chunking to enhance retrieval accuracy.
How It Works
Embeddings create dense numerical representations of text, allowing them to tolerate typos by understanding context. For instance, similar words like 'policy' and 'polciy' are close in vector space due to their training on similar contexts, enabling effective retrieval despite errors.