Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
Data Science
Towards Data Science·20d ago·5 min read
Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent
TL;DR
RAG (Retrieval-Augmented Generation) systems often produce hallucinations due to retrieval failures rather than generation issues. By improving the retrieval process, models can avoid being misled by irrelevant or incorrect context.
✦ Why It Matters
Engineers should prioritize refining retrieval methods to reduce hallucinations in RAG systems today.
Key Takeaways
- Implement keyword matching to improve retrieval accuracy.
- Scope context tightly around the relevant answer.
- Measure retrieval ranks to diagnose hallucination issues.
- Use structured documents to enhance retrieval effectiveness.
How It Works
The retrieval process uses cosine similarity to rank document pages based on their relevance to a question. However, this method can misinterpret context, especially in structured documents where key terms may be diluted by surrounding text.
By focusing on keyword matching and the document's structure, the retrieval can be improved, ensuring that the most relevant information is presented to the model.