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cs.IR2025
Exploring Training and Inference Scaling Laws in Generative Retrieval
Hongru Cai, Yongqi Li, Ruifeng Yuan +4
Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models (LLMs) generate target documents directly from a query. As a novel par…
cs.IR2025
Unsupervised Query Routing for Retrieval Augmented Generation
Feiteng Mu, Liwen Zhang, Yong Jiang +4
Query routing for retrieval-augmented generation aims to assign an input query to the most suitable search engine. Existing works rely heavily on supervised datasets that require e…