Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding
<|reference_start|>Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding: 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.<|reference_end|>
@article{zeng2024planning,
    title={Planning Ahead in Generative Retrieval: Guiding Autoregressive
  Generation through Simultaneous Decoding},
    author={Hansi Zeng, Chen Luo, Hamed Zamani},
    journal={arXiv preprint arXiv:2404.14600},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14600},
    primaryClass={cs.IR cs.CL}
}