MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge base could inevitably introduce irrelevant information that may mislead generation for complex reasoning. Inspired by the conditional computation of mixture of experts (MoE), where a router sparsely selects specialized experts alongside shared ones for each input, we propose \textbf{M}ixture \textbf{o}f experts for \textbf{G}raph-based Retrieval-Augmented Generation, i.e., \textbf{MoG}. It organizes knowledge into two core components: (i) diverse, always-accessible hub graphs that encode semantically and structurally central knowledge and...
| Authors | Zheng Yuan, Chuang Zhou, Linhao Luo et al. |
|---|---|
| Primary category | cs.CL |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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