Generative Retrieval

Generative retrieval studies how to find documents, products, or other items by generating their identifiers directly from a query rather than ranking a precomputed candidate set. Many systems encode items as semantic IDs, then use autoregressive or diffusion models with constrained decoding; training can combine supervised examples, contrastive objectives, and reinforcement learning from relevance or user-preference signals. The approach is being applied to web and e-commerce search, recommendation, legal and multimodal retrieval, where it can simplify retrieval pipelines or incorporate richer context. Key challenges include preserving exact-match recall and identifier quality, controlling decoding cost and errors, and establishing fair comparisons with dense retrieval systems.