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HansiZeng · Dec 5, 2023 · 62aafc6 · 62aafc6
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 # Scalable and Effective Generative Information Retrieval
-This repo provides the source code and checkpoints for our paper [Scalable and Effective Generative Information Retrieval]() (RIPOR). We propose RIPOR, a optimization framework for generative retrieval. RIPOR is designed based on two often-overlooked fundamental design considerations in generative retrieval. To addresse the issues, we propose a novel prefix-oriented ranking optimization algorithm and relevance-based DocID initialization, which illustrated in the following Figure. The main experiment is conducted on large-scale information retrieval benchmark MSMARCO-8.8M, and evaluated on three evaluation sets MSMARCO-Dev, TREC'19 and 20. RIPOR surpasses state-of-the-art generative retrieval models by a large margin (e.g., 30.5% MRR improvements on MS MARCO Dev Set), and perform better on par with popular dense retrieval models.
+This repo provides the source code and checkpoints for our paper [Scalable and Effective Generative Information Retrieval](https://arxiv.org/pdf/2311.09134.pdf) (RIPOR). We propose RIPOR, a optimization framework for generative retrieval. RIPOR is designed based on two often-overlooked fundamental design considerations in generative retrieval. To addresse the issues, we propose a novel prefix-oriented ranking optimization algorithm and relevance-based DocID initialization, which illustrated in the following Figure. The main experiment is conducted on large-scale information retrieval benchmark MSMARCO-8.8M, and evaluated on three evaluation sets MSMARCO-Dev, TREC'19 and 20. RIPOR surpasses state-of-the-art generative retrieval models by a large margin (e.g., 30.5% MRR improvements on MS MARCO Dev Set), and perform better on par with popular dense retrieval models.
 
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   <img align="center" src="./arch.png" width="850" />