  Pseudo-relevance feedback (PRF) is a classical approach to address lexical
mismatch by enriching the query using first-pass retrieval. Moreover, recent
work on generative-relevance feedback (GRF) shows that query expansion models
using text generated from large language models can improve sparse retrieval
without depending on first-pass retrieval effectiveness. This work extends GRF
to dense and learned sparse retrieval paradigms with experiments over six
standard document ranking benchmarks. We find that GRF improves over comparable
PRF techniques by around 10% on both precision and recall-oriented measures.
Nonetheless, query analysis shows that GRF and PRF have contrasting benefits,
with GRF providing external context not present in first-pass retrieval,
whereas PRF grounds the query to the information contained within the target
corpus. Thus, we propose combining generative and pseudo-relevance feedback
ranking signals to achieve the benefits of both feedback classes, which
significantly increases recall over PRF methods on 95% of experiments.
