  In large-scale retrieval, the lexicon-weighting paradigm, learning weighted
sparse representations in vocabulary space, has shown promising results with
high quality and low latency. Despite it deeply exploiting the
lexicon-representing capability of pre-trained language models, a crucial gap
remains between language modeling and lexicon-weighting retrieval -- the former
preferring certain or low-entropy words whereas the latter favoring pivot or
high-entropy words -- becoming the main barrier to lexicon-weighting
performance for large-scale retrieval. To bridge this gap, we propose a
brand-new pre-training framework, lexicon-bottlenecked masked autoencoder
(LexMAE), to learn importance-aware lexicon representations. Essentially, we
present a lexicon-bottlenecked module between a normal language modeling
encoder and a weakened decoder, where a continuous bag-of-words bottleneck is
constructed to learn a lexicon-importance distribution in an unsupervised
fashion. The pre-trained LexMAE is readily transferred to the lexicon-weighting
retrieval via fine-tuning. On the ad-hoc retrieval benchmark, MS-Marco, it
achieves 42.6% MRR@10 with 45.8 QPS for the passage dataset and 44.4% MRR@100
with 134.8 QPS for the document dataset, by a CPU machine. And LexMAE shows
state-of-the-art zero-shot transfer capability on BEIR benchmark with 12
datasets.
