This paper considers Pseudo-Relevance Feedback (PRF) methods for dense retrievers in a resource constrained environment such as that of cheap cloud instances or embedded systems (e.g., smartphones and smartwatches), where memory and CPU are limited and GPUs are not present. For this, we propose a transformer-based PRF method (TPRF), which has a much smaller memory footprint and faster inference time compared to other deep language models that employ PRF mechanisms, with a marginal effectiveness loss. TPRF learns how to effectively combine the relevance feedback signals from dense passage representations. Specifically, TPRF provides a mechanism for modelling relationships and weights between the query and the relevance feedback signals. The method is agnostic to the specific dense representation used and thus can be generally applied to any dense retriever.

翻译：本文研究了在资源受限环境（如廉价云实例或嵌入式系统，例如智能手机和智能手表）中密集检索器的伪相关反馈方法。在此类环境中，内存和CPU有限，且不配备GPU。为此，我们提出了一种基于Transformer的PRF方法（TPRF），与其他采用PRF机制的深度语言模型相比，该方法内存占用更小、推理时间更快，而效果损失极小。TPRF能够学习如何有效结合来自密集段落表示的相关性反馈信号。具体而言，TPRF提供了一种对查询与相关性反馈信号之间的关系和权重进行建模的机制。该方法不依赖于特定的密集表示，因此可普遍应用于任何密集检索器。