RouterRetriever: Exploring the Benefits of Routing over Multiple Expert Embedding Models

• URL: http://arxiv.org/abs/2409.02685v1
• Date: Wed, 4 Sep 2024 13:16:55 GMT
• Title: RouterRetriever: Exploring the Benefits of Routing over Multiple Expert Embedding Models
• Authors: Hyunji Lee, Luca Soldaini, Arman Cohan, Minjoon Seo, Kyle Lo,
• Abstract summary: We introduce RouterRetriever, a retrieval model that leverages multiple domain-specific experts. It is lightweight and allows easy addition or removal of experts without additional training. It is the first work to demonstrate the advantages of using multiple domain-specific expert embedding models.
• Score: 58.987116118425995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, models trained on domain-specific data often yield better results within their respective domains. While prior work in information retrieval has tackled this through multi-task training, the topic of combining multiple domain-specific expert retrievers remains unexplored, despite its popularity in language model generation. In this work, we introduce RouterRetriever, a retrieval model that leverages multiple domain-specific experts along with a routing mechanism to select the most appropriate expert for each query. It is lightweight and allows easy addition or removal of experts without additional training. Evaluation on the BEIR benchmark demonstrates that RouterRetriever outperforms both MSMARCO-trained (+2.1 absolute nDCG@10) and multi-task trained (+3.2) models. This is achieved by employing our routing mechanism, which surpasses other routing techniques (+1.8 on average) commonly used in language modeling. Furthermore, the benefit generalizes well to other datasets, even in the absence of a specific expert on the dataset. To our knowledge, RouterRetriever is the first work to demonstrate the advantages of using multiple domain-specific expert embedding models with effective routing over a single, general-purpose embedding model in retrieval tasks.

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