Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage Retrieval (2208.00511v2)

Abstract: Pre-trained LLMs have been successful in many knowledge-intensive NLP tasks. However, recent work has shown that models such as BERT are not structurally ready'' to aggregate textual information into a [CLS] vector for dense passage retrieval (DPR). Thislack of readiness'' results from the gap between LLM pre-training and DPR fine-tuning. Previous solutions call for computationally expensive techniques such as hard negative mining, cross-encoder distillation, and further pre-training to learn a robust DPR model. In this work, we instead propose to fully exploit knowledge in a pre-trained LLM for DPR by aggregating the contextualized token embeddings into a dense vector, which we call agg*. By concatenating vectors from the [CLS] token and agg*, our Aggretriever model substantially improves the effectiveness of dense retrieval models on both in-domain and zero-shot evaluations without introducing substantial training overhead. Code is available at https://github.com/castorini/dhr