  This paper describes our participation in the TREC 2023 Deep Learning Track.
We submitted runs that apply generative relevance feedback from a large
language model in both a zero-shot and pseudo-relevance feedback setting over
two sparse retrieval approaches, namely BM25 and SPLADE. We couple this first
stage with adaptive re-ranking over a BM25 corpus graph scored using a
monoELECTRA cross-encoder. We investigate the efficacy of these generative
approaches for different query types in first-stage retrieval. In re-ranking,
we investigate operating points of adaptive re-ranking with different first
stages to find the point in graph traversal where the first stage no longer has
an effect on the performance of the overall retrieval pipeline. We find some
performance gains from the application of generative query reformulation.
However, our strongest run in terms of P@10 and nDCG@10 applied both adaptive
re-ranking and generative pseudo-relevance feedback, namely uogtr_b_grf_e_gb.
