  We develop a method for training small-scale (under 100M parameter) neural
information retrieval models with as few as 10 gold relevance labels. The
method depends on generating synthetic queries for documents using a language
model (LM), and the key step is that we automatically optimize the LM prompt
that is used to generate these queries based on training quality. In
experiments with the BIRCO benchmark, we find that models trained with our
method outperform RankZephyr and are competitive with RankLLama, both of which
are 7B parameter models trained on over 100K labels. These findings point to
the power of automatic prompt optimization for synthetic dataset generation.
