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Data Science
Towards Data Science·Jun 24·5 min read
Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End
TL;DR
Retrieving relevant information from documents can be challenging due to the need for precise context. A three-stage anchor-detection pipeline was developed, utilizing parallel keyword and embedding detectors followed by a single large language model (LLM) call for ranking.
✦ Why It Matters
Engineers can implement a multi-stage retrieval process to enhance document search accuracy and context understanding.
Key Takeaways
- Utilize parallel processing for keyword and embedding detection.
- Implement a three-stage retrieval pipeline for better accuracy.
- Incorporate LLMs for contextual ranking of document sections.
- Provide clear rationales for retrieval decisions to aid auditing.
Full Summary
In enterprise document retrieval, accurately identifying relevant sections of text is crucial for effective information extraction. A novel anchor-detection pipeline was created, which operates in three stages: first, it runs keyword detection and embedding analysis in parallel to identify candidate documents; second, it aggregates these results into a structural unit; and finally, it employs a single large language model (LLM) call to rank the candidates based on contextual relevance.
For example, when a user queries about 'attention computation' in a Transformer paper, both keyword and embedding methods identify potential matches, but the LLM ultimately selects the most relevant section. This method enhances retrieval accuracy by providing a rationale for the selected answer, making it easier for auditors to understand the decision later.
The implications of this approach suggest that engineers can build more effective retrieval systems that leverage both traditional and modern AI techniques.