  Web search provides a promising way for people to obtain information and has
been extensively studied. With the surgence of deep learning and large-scale
pre-training techniques, various neural information retrieval models are
proposed and they have demonstrated the power for improving search (especially,
the ranking) quality. All these existing search methods follow a common
paradigm, i.e. index-retrieve-rerank, where they first build an index of all
documents based on document terms (i.e., sparse inverted index) or
representation vectors (i.e., dense vector index), then retrieve and rerank
retrieved documents based on similarity between the query and documents via
ranking models. In this paper, we explore a new paradigm of information
retrieval with neither sparse nor dense index but only a model. Specifically,
we propose a pre-training model-based IR system called DynamicRetriever. As for
this system, the training stage embeds the token-level and document-level
information (especially, document identifiers) of the corpus into the model
parameters, then the inference stage directly generates document identifiers
for a given query. Compared with existing search methods, the model-based IR
system has two advantages: i) it parameterizes the traditional static index
with a pre-training model, which converts the document semantic mapping into a
dynamic and updatable process; ii) with separate document identifiers, it
captures both the term-level and document-level information for each document.
Extensive experiments conducted on the public search benchmark MS MARCO verify
the effectiveness and potential of our proposed new paradigm for information
retrieval.
