Papers › MetaGen Blended RAG: Higher Accuracy for Domain-Specific Q&A Without Fine-Tuning
MetaGen Blended RAG: Higher Accuracy for Domain-Specific Q&A Without Fine-Tuning
Kunal Sawarkar, Shivam R. Solanki, Abhilasha Mangal
Despite the widespread exploration of Retrieval-Augmented Generation (RAG), its deployment in enterprises for domain-specific datasets remains limited due to poor answer accuracy. These corpora, often shielded behind firewalls in private enterprise knowledge bases, having complex, domain-specific terminology, rarely seen by LLMs during pre-training; exhibit significant semantic variability across domains (like networking, military, or legal, etc.), or even within a single domain like medicine, and thus result in poor context precision for RAG systems. Currently, in such situations, fine-tuning or RAG with fine-tuning is attempted, but these approaches are slow, expensive, and lack generalization for accuracy as the new domain-specific data emerges. We propose an approach for Enterprise Search that focuses on enhancing the retriever for a domain-specific corpus through hybrid query indexes and metadata enrichment. This 'MetaGen Blended RAG' method constructs a metadata generation pipeline using key concepts, topics, and acronyms, and then creates a metadata-enriched hybrid index with boosted search queries. This approach avoids overfitting and generalizes effectively across domains. On the PubMedQA benchmark for the biomedical domain, the proposed method achieves 82% retrieval accuracy and 77% RAG accuracy, surpassing all previous RAG accuracy results without fine-tuning and sets a new benchmark for zero-shot results while outperforming much larger models like GPT3.5. The results are even comparable to the best fine-tuned models on this dataset, and we further demonstrate the robustness and scalability of the approach by evaluating it on other Q&A datasets like SQuAD, NQ etc.
Code
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Tasks
Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Learning | PubMedQA | MetaGen Blended RAG (zero-shot) | Accuracy | 77.9 | #1 of 2 | Archive leaderboard | report |
| Question Answering | PubMedQA | MetaGen Blended RAG (zero-shot) | Accuracy | 77.9 | #9 of 30 | Archive leaderboard | report |
| RAG | PubMedQA corpus with metadata | MetaGen Blended RAG | ANS-EM | 77.90 | #1 of 1 | Archive leaderboard | report |
| Retrieval | PubMedQA | MetaGen Blended RAG | Accuracy (Top-1) | 82.1 | #1 of 1 | Archive leaderboard | report |
| Retrieval | PubMedQA corpus with metadata | MetaGen Blended RAG | Accuracy (Top-1) | 82.1 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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