小模型加大模型重排，反而比大模型更有效。
Rethinking Hybrid Retrieval: When Small Embeddings and LLM Re-ranking Beat Bigger Models
- 三模态融合+大模型重排，小模型表现更优
- 在三个数据集上检索效果显著提升
- 适合追求效率与精度的RAG系统
本文对比了三模态混合检索中嵌入模型的表现，融合密集语义、稀疏词法和图嵌入。研究聚焦MiniLM-v6与BGE-Large架构，在SciFact、FIQA和NFCorpus数据集上的实验表明，尽管体积更小，MiniLM-v6在结合大语言模型重排后，整体检索性能优于更大的BGE-Large。尤其在代理式重排场景下差异显著，说明其嵌入空间与大模型推理更契合。结果表明，RAG系统选型应重视多信号融合兼容性与大模型对齐能力，而非单纯依赖模型规模。该方法可在降低计算开销的同时提升检索准确率与效率。
原文摘要 · Abstract (English)
This paper presents a comparison of embedding models in tri-modal hybrid retrieval for Retrieval-Augmented Generation (RAG) systems. We investigate the fusion of dense semantic, sparse lexical, and graph-based embeddings, focusing on the performance of the MiniLM-v6 and BGE-Large architectures. Contrary to conventional assumptions, our results show that the compact MiniLM-v6 outperforms the larger BGE-Large when integrated with LLM-based re-ranking within our tri-modal hybrid framework. Experiments conducted on the SciFact, FIQA, and NFCorpus datasets demonstrate significant improvements in retrieval quality with the MiniLM-v6 configuration. The performance difference is particularly pronounced in agentic re-ranking scenarios, indicating better alignment between MiniLM-v6's embedding space and LLM reasoning. Our findings suggest that embedding model selection for RAG systems should prioritize compatibility with multi-signal fusion and LLM alignment, rather than relying solely on larger models. This approach may reduce computational requirements while improving retrieval accuracy and efficiency.
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