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This paper considers Pseudo-Relevance Feedback (PRF) methods for denseretrievers in a resource constrained environment such as that of cheap cloudinstances or embedded systems (e.g., smartphones and smartwatches), wherememory and CPU are limited and GPUs are not present. For this, we propose atransformer-based PRF method (TPRF), which has a much smaller memory footprintand faster inference time compared to other deep language models that employPRF mechanisms, with a marginal effectiveness loss. TPRF learns how toeffectively combine the relevance feedback signals from dense passagerepresentations. Specifically, TPRF provides a mechanism for modellingrelationships and weights between the query and the relevance feedback signals.The method is agnostic to the specific dense representation used and thus canbe generally applied to any dense retriever.

TPRF: A Transformer-based Pseudo-Relevance Feedback Model for Efficient and Effective Retrieval