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Pre-trained language models have been successful in many knowledge-intensiveNLP tasks. However, recent work has shown that models such as BERT are not``structurally ready'' to aggregate textual information into a [CLS] vector fordense passage retrieval (DPR). This ``lack of readiness'' results from the gapbetween language model pre-training and DPR fine-tuning. Previous solutionscall for computationally expensive techniques such as hard negative mining,cross-encoder distillation, and further pre-training to learn a robust DPRmodel. In this work, we instead propose to fully exploit knowledge in apre-trained language model for DPR by aggregating the contextualized tokenembeddings into a dense vector, which we call agg*. By concatenating vectorsfrom the [CLS] token and agg*, our Aggretriever model substantially improvesthe effectiveness of dense retrieval models on both in-domain and zero-shotevaluations without introducing substantial training overhead. Code isavailable at https://github.com/castorini/dhr

Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage Retrieval