Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study

Understand

Pseudo-Relevance Feedback (PRF) utilises the relevance signals from the top-k passages from the first round of retrieval to perform a second round of retrieval aiming to improve search effectiveness.

• A recent research direction has been the study and development of PRF methods for deep language models based rankers, and in particular in the context of dense retrievers.
• Dense retrievers, compared to more complex neural rankers, provide a trade-off between effectiveness, which is often reduced compared to more complex neural rankers, and query latency, which also is reduced making the retrieval pipeline more efficient.
• The introduction of PRF methods for dense retrievers has been motivated as an attempt to further improve their effectiveness.