LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval
This addresses the need for effective text retrieval systems that don't require labeled training data, representing a significant advance rather than an incremental improvement.
The authors tackled the problem of zero-shot text retrieval without supervised training data by proposing LaPraDoR, an unsupervised pretrained dense retriever that achieves state-of-the-art performance on the BEIR benchmark across 18 datasets, with their lexicon-enhanced approach running 22.5x faster than re-ranking methods.
In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever that does not require any supervised data for training. Specifically, we first present Iterative Contrastive Learning (ICoL) that iteratively trains the query and document encoders with a cache mechanism. ICoL not only enlarges the number of negative instances but also keeps representations of cached examples in the same hidden space. We then propose Lexicon-Enhanced Dense Retrieval (LEDR) as a simple yet effective way to enhance dense retrieval with lexical matching. We evaluate LaPraDoR on the recently proposed BEIR benchmark, including 18 datasets of 9 zero-shot text retrieval tasks. Experimental results show that LaPraDoR achieves state-of-the-art performance compared with supervised dense retrieval models, and further analysis reveals the effectiveness of our training strategy and objectives. Compared to re-ranking, our lexicon-enhanced approach can be run in milliseconds (22.5x faster) while achieving superior performance.