  Vector quantization (VQ) based ANN indexes, such as Inverted File System
(IVF) and Product Quantization (PQ), have been widely applied to embedding
based document retrieval thanks to the competitive time and memory efficiency.
Originally, VQ is learned to minimize the reconstruction loss, i.e., the
distortions between the original dense embeddings and the reconstructed
embeddings after quantization. Unfortunately, such an objective is inconsistent
with the goal of selecting ground-truth documents for the input query, which
may cause severe loss of retrieval quality. Recent works identify such a
defect, and propose to minimize the retrieval loss through contrastive
learning. However, these methods intensively rely on queries with ground-truth
documents, whose performance is limited by the insufficiency of labeled data.
  In this paper, we propose Distill-VQ, which unifies the learning of IVF and
PQ within a knowledge distillation framework. In Distill-VQ, the dense
embeddings are leveraged as "teachers", which predict the query's relevance to
the sampled documents. The VQ modules are treated as the "students", which are
learned to reproduce the predicted relevance, such that the reconstructed
embeddings may fully preserve the retrieval result of the dense embeddings. By
doing so, Distill-VQ is able to derive substantial training signals from the
massive unlabeled data, which significantly contributes to the retrieval
quality. We perform comprehensive explorations for the optimal conduct of
knowledge distillation, which may provide useful insights for the learning of
VQ based ANN index. We also experimentally show that the labeled data is no
longer a necessity for high-quality vector quantization, which indicates
Distill-VQ's strong applicability in practice.
