Dense retrievers encode documents into fixed dimensional embeddings. However,
storing all the document embeddings within an index produces bulky indexes
which are expensive to serve. Recently, BPR (Yamada et al., 2021) and JPQ (Zhan
et al., 2021a) have been proposed which train the model to produce binary
document vectors, which reduce the index 32x and more. The authors showed these
binary embedding models significantly outperform more traditional index
compression techniques like Product Quantization (PQ). Previous work evaluated
these approaches just in-domain, i.e. the methods were evaluated on tasks for
which training data is available. In practice, retrieval models are often used
in an out-of-domain setting, where they have been trained on a publicly
available dataset, like MS MARCO, but are then used for some custom dataset for
which no training data is available.
In this work, we show that binary embedding models like BPR and JPQ can
perform significantly worse than baselines once there is a domain-shift
involved. We propose a modification to the training procedure of BPR and JPQ
and combine it with a corpus specific generative procedure which allow the
adaptation of BPR and JPQ to any corpus without requiring labeled training
data. Our domain-adapted strategy known as GPL is model agnostic, achieves an
improvement by up-to 19.3 and 11.6 points in nDCG@10 across the BEIR benchmark
in comparison to BPR and JPQ while maintaining its 32x memory efficiency.
JPQ+GPL even outperforms our upper baseline: uncompressed TAS-B model on
average by 2.0 points.

本文探讨了二进制文档向量的性能及传统压缩技术的局限性，并提出了一个基于 GPL 的领域自适应策略，可在不需要标注训练数据的情况下，将 BPR 和 JPQ 调整适应任何语料库，提高了 nDCG@10 平均 11.6 - 19.3 分，并且仍保持了 32 倍的内存效率。