Routerretriever: Routing Over A Mixture Of Expert Embedding Models

Abstract

Information retrieval methods often rely on a single embedding model trained on large, general-domain datasets like MSMARCO. While this approach can produce a retriever with reasonable overall performance, they often underperform models trained on domain-specific data when testing on their respective domains. Prior work in information retrieval has tackled this through multi-task training, but the idea of routing over a mixture of domain-specific expert retrievers remains unexplored despite the popularity of such ideas in language model generation research. In this work, we introduce RouterRetriever, a retrieval model that leverages a mixture of domain-specific experts by using a routing mechanism to select the most appropriate expert for each query. RouterRetriever is lightweight and allows easy addition or removal of experts without additional training. Evaluation on the BEIR benchmark demonstrates that RouterRetriever outperforms both models trained on MSMARCO (+2.1 absolute nDCG@10

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keylee2024routerretriever

Related papers

• Investigating Mixture Of Experts In Dense Retrieval (2024)0.00
• Mixture Of Experts Approaches In Dense Retrieval Tasks (2025)0.95
• CAME: Competitively Learning A Mixture-of-experts Model For First-stage Retrieval (2023)6.34
• Mor: Better Handling Diverse Queries With A Mixture Of Sparse, Dense, And Human Retrievers (2025)2.26
• Freeret: Mllms As Training-free Retrievers (2025)0.00
• Investigating Multi-layer Representations For Dense Passage Retrieval (2025)0.00
• LMAR: Language Model Augmented Retriever For Domain-specific Knowledge Indexing (2025)1.57
• Lamra: Large Multimodal Model As Your Advanced Retrieval Assistant (2024)7.50