Semi-Parametric Retrieval via Binary Token Index
<|reference_start|>Semi-Parametric Retrieval via Binary Token Index: The landscape of information retrieval has broadened from search services to a critical component in various advanced applications, where indexing efficiency, cost-effectiveness, and freshness are increasingly important yet remain less explored. To address these demands, we introduce Semi-parametric Vocabulary Disentangled Retrieval (SVDR). SVDR is a novel semi-parametric retrieval framework that supports two types of indexes: an embedding-based index for high effectiveness, akin to existing neural retrieval methods; and a binary token index that allows for quick and cost-effective setup, resembling traditional term-based retrieval. In our evaluation on three open-domain question answering benchmarks with the entire Wikipedia as the retrieval corpus, SVDR consistently demonstrates superiority. It achieves a 3% higher top-1 retrieval accuracy compared to the dense retriever DPR when using an embedding-based index and an 9% higher top-1 accuracy compared to BM25 when using a binary token index. Specifically, the adoption of a binary token index reduces index preparation time from 30 GPU hours to just 2 CPU hours and storage size from 31 GB to 2 GB, achieving a 90% reduction compared to an embedding-based index.<|reference_end|>
@article{zhou2024semi-parametric,
    title={Semi-Parametric Retrieval via Binary Token Index},
    author={Jiawei Zhou, Li Dong, Furu Wei, Lei Chen},
    journal={arXiv preprint arXiv:2405.01924},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01924},
    primaryClass={cs.CL cs.AI cs.IR}
}