无需微调即可在专业领域实现高精度检索，解决企业数据难用问题。
MetaGen Blended RAG: Unlocking Zero-Shot Precision for Specialized Domain Question-Answering
- 通过元数据生成与稠密/稀疏向量混合索引提升检索能力。
- 在PubMedQA上达82%检索准确率，77%RAG准确率，超越所有零样本基准。
- 适合医疗、法律等专业领域，无需微调即可快速部署。
检索增强生成（RAG）在企业专有数据集上表现受限，这些数据常受防火墙保护，且包含大量预训练阶段未见的复杂术语。医学、网络、法律等领域语义差异大，导致RAG上下文精度下降；而微调方案成本高、速度慢，且难以适应新数据。实现零样本下无微调的精确检索仍是关键挑战。本文提出MetaGen Blended RAG，一种新型企业搜索方法，通过元数据生成管道和稠密-稀疏向量混合查询索引，利用关键概念、主题和缩写构建增强语义索引与强化混合查询，实现无需微调的稳健可扩展性能。在生物医学PubMedQA数据集上，该方法达到82%检索准确率和77% RAG准确率，超越所有先前零样本RAG基准，甚至媲美微调模型；同时在SQuAD和NQ等数据集上也表现优异。该方法重新定义了专用领域语义检索范式。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) struggles with domain-specific enterprise datasets, often isolated behind firewalls and rich in complex, specialized terminology unseen by LLMs during pre-training. Semantic variability across domains like medicine, networking, or law hampers RAG's context precision, while fine-tuning solutions are costly, slow, and lack generalization as new data emerges. Achieving zero-shot precision with retrievers without fine-tuning still remains a key challenge. We introduce 'MetaGen Blended RAG', a novel enterprise search approach that enhances semantic retrievers through a metadata generation pipeline and hybrid query indexes using dense and sparse vectors. By leveraging key concepts, topics, and acronyms, our method creates metadata-enriched semantic indexes and boosted hybrid queries, delivering robust, scalable performance without fine-tuning. On the biomedical PubMedQA dataset, MetaGen Blended RAG achieves 82% retrieval accuracy and 77% RAG accuracy, surpassing all prior zero-shot RAG benchmarks and even rivaling fine-tuned models on that dataset, while also excelling on datasets like SQuAD and NQ. This approach redefines enterprise search using a new approach to building semantic retrievers with unmatched generalization across specialized domains.
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