  Performing automatic reformulations of a user's query is a popular paradigm
used in information retrieval (IR) for improving effectiveness -- as
exemplified by the pseudo-relevance feedback approaches, which expand the query
in order to alleviate the vocabulary mismatch problem. Recent advancements in
generative language models have demonstrated their ability in generating
responses that are relevant to a given prompt. In light of this success, we
seek to study the capacity of such models to perform query reformulation and
how they compare with long-standing query reformulation methods that use
pseudo-relevance feedback. In particular, we investigate two representative
query reformulation frameworks, GenQR and GenPRF. GenQR directly reformulates
the user's input query, while GenPRF provides additional context for the query
by making use of pseudo-relevance feedback information. For each reformulation
method, we leverage different techniques, including fine-tuning and direct
prompting, to harness the knowledge of language models. The reformulated
queries produced by the generative models are demonstrated to markedly benefit
the effectiveness of a state-of-the-art retrieval pipeline on four TREC test
collections (varying from TREC 2004 Robust to the TREC 2019 Deep Learning).
Furthermore, our results indicate that our studied generative models can
outperform various statistical query expansion approaches while remaining
comparable to other existing complex neural query reformulation models, with
the added benefit of being simpler to implement.
