Mitigating Zero-Shot Retrieval Performance Gaps via Domain-Specific Fine-Tuning of Dense Retrievers on BEIR Datasets

Abstract

Abstract: We propose the new problem of choosing which dense retrieval model to use when searching on a new collection for which no labels are available, i.e. in a zero-shot setting. Many dense retrieval models are readily available. Each model however is characterized by very differing search effectiveness – not just on the test portion of the datasets in which the dense representations have been learned but, importantly, also across different datasets for which data was not used to learn the dense representations. This is because dense retrievers typically require training on a large amount of labele

Research Question

To what extent does domain-specific fine-tuning of dense retrievers on BEIR datasets mitigate the performance gap observed in zero-shot retrieval when compared to pre-trained models without fine-tuning?

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