Recent developments of dense retrieval rely on quality representations of queries and contexts from pre-trained query and context encoders. In this paper, we introduce TOUR (Test-Time Optimization of Query Representations), which further optimizes instance-level query representations guided by signals from test-time retrieval results. We leverage a cross-encoder re-ranker to provide fine-grained pseudo labels over retrieval results and iteratively optimize query representations with gradient descent. Our theoretical analysis reveals that TOUR can be viewed as a generalization of the classical Rocchio algorithm for pseudo relevance feedback, and we present two variants that leverage pseudo-labels as hard binary or soft continuous labels. We first apply TOUR on phrase retrieval with our proposed phrase re-ranker, and also evaluate its effectiveness on passage retrieval with an off-the-shelf re-ranker. TOUR greatly improves end-to-end open-domain question answering accuracy, as well as passage retrieval performance. TOUR also consistently improves direct re-ranking by up to 2.0% while running 1.3-2.4x faster with an efficient implementation.

翻译：近期稠密检索的发展依赖于从预训练查询编码器和上下文编码器中获得高质量查询与上下文表示。本文提出TOUR（测试时查询表示优化），该方法借助测试时检索结果的信号，进一步优化实例级别的查询表示。我们利用交叉编码器重排序器为检索结果提供细粒度伪标签，并通过梯度下降迭代优化查询表示。理论分析表明，TOUR可视为经典Rocchio伪相关反馈算法的广义形式，同时我们提出两种变体，分别将伪标签用作硬二值标签与软连续标签。我们首先将TOUR应用于短语检索（配合提出的短语重排序器），并在段落检索中通过现成重排序器验证其有效性。TOUR显著提升了端到端开放域问答准确率及段落检索性能。此外，在高效实现下，TOUR持续改进直接重排序效果（最高提升2.0%），同时运行速度提升1.3-2.4倍。