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arXiv · 2404.14600

Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding

Hansi Zeng·Chen Luo·Hamed Zamani
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Abstract

This paper introduces PAG-a novel optimization and decoding approach that guides autoregressive generation of document identifiers in generative retrieval models through simultaneous decoding. To this aim, PAG constructs a set-based and sequential identifier for each document. Motivated by the bag-of-words assumption in information retrieval, the set-based identifier is built on lexical tokens. The sequential identifier, on the other hand, is obtained via quantizing relevance-based representations of documents. Extensive experiments on MSMARCO and TREC Deep Learning Track data reveal that PAG outperforms the state-of-the-art generative retrieval model by a large margin (e.g., 15.6% MRR improvements on MS MARCO), while achieving 22x speed up in terms of query latency.

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Hansi Zeng, Chen Luo, Hamed Zamani. 2024-04-22. Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding. https://arxiv.org/abs/2404.14600

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