  Test collections play a vital role in evaluation of information retrieval
(IR) systems. Obtaining a diverse set of user queries for test collection
construction can be challenging, and acquiring relevance judgments, which
indicate the appropriateness of retrieved documents to a query, is often costly
and resource-intensive. Generating synthetic datasets using Large Language
Models (LLMs) has recently gained significant attention in various
applications. In IR, while previous work exploited the capabilities of LLMs to
generate synthetic queries or documents to augment training data and improve
the performance of ranking models, using LLMs for constructing synthetic test
collections is relatively unexplored. Previous studies demonstrate that LLMs
have the potential to generate synthetic relevance judgments for use in the
evaluation of IR systems. In this paper, we comprehensively investigate whether
it is possible to use LLMs to construct fully synthetic test collections by
generating not only synthetic judgments but also synthetic queries. In
particular, we analyse whether it is possible to construct reliable synthetic
test collections and the potential risks of bias such test collections may
exhibit towards LLM-based models. Our experiments indicate that using LLMs it
is possible to construct synthetic test collections that can reliably be used
for retrieval evaluation.
