  There has been limited success for dense retrieval models in multilingual
retrieval, due to uneven and scarce training data available across multiple
languages. Synthetic training data generation is promising (e.g., InPars or
Promptagator), but has been investigated only for English. Therefore, to study
model capabilities across both cross-lingual and monolingual retrieval tasks,
we develop SWIM-IR, a synthetic retrieval training dataset containing 33 (high
to very-low resource) languages for fine-tuning multilingual dense retrievers
without requiring any human supervision. To construct SWIM-IR, we propose SAP
(summarize-then-ask prompting), where the large language model (LLM) generates
a textual summary prior to the query generation step. SAP assists the LLM in
generating informative queries in the target language. Using SWIM-IR, we
explore synthetic fine-tuning of multilingual dense retrieval models and
evaluate them robustly on three retrieval benchmarks: XOR-Retrieve
(cross-lingual), MIRACL (monolingual) and XTREME-UP (cross-lingual). Our
models, called SWIM-X, are competitive with human-supervised dense retrieval
models, e.g., mContriever-X, finding that SWIM-IR can cheaply substitute for
expensive human-labeled retrieval training data. SWIM-IR dataset and SWIM-X
models are available at https://github.com/google-research-datasets/SWIM-IR.
