  We describe team ielab from CSIRO and The University of Queensland's approach
to the 2023 TREC Clinical Trials Track. Our approach was to use neural rankers
but to utilise Large Language Models to overcome the issue of lack of training
data for such rankers. Specifically, we employ ChatGPT to generate relevant
patient descriptions for randomly selected clinical trials from the corpus.
This synthetic dataset, combined with human-annotated training data from
previous years, is used to train both dense and sparse retrievers based on
PubmedBERT. Additionally, a cross-encoder re-ranker is integrated into the
system. To further enhance the effectiveness of our approach, we prompting
GPT-4 as a TREC annotator to provide judgments on our run files. These
judgments are subsequently employed to re-rank the results. This architecture
tightly integrates strong PubmedBERT-based rankers with the aid of SOTA Large
Language Models, demonstrating a new approach to clinical trial retrieval.
