  Doc2Query -- the process of expanding the content of a document before
indexing using a sequence-to-sequence model -- has emerged as a prominent
technique for improving the first-stage retrieval effectiveness of search
engines. However, sequence-to-sequence models are known to be prone to
"hallucinating" content that is not present in the source text. We argue that
Doc2Query is indeed prone to hallucination, which ultimately harms retrieval
effectiveness and inflates the index size. In this work, we explore techniques
for filtering out these harmful queries prior to indexing. We find that using a
relevance model to remove poor-quality queries can improve the retrieval
effectiveness of Doc2Query by up to 16%, while simultaneously reducing mean
query execution time by 23% and cutting the index size by 33%. We release the
code, data, and a live demonstration to facilitate reproduction and further
exploration at https://github.com/terrierteam/pyterrier_doc2query.
