How does COCO-DR's continuous contrastive pretraining on target corpora affect zero-shot accuracy on BEIR benc

Abstract

Abstract: In recent years, foundation models have become very popular due to their exceptional performance, mainly in natural language (NLP) tasks where they were first introduced. These models usually consist of hundreds of millions, or even billions, of parameters, making them resource-intensive during training and in production systems, leading to increased costs. This paper focuses on the reduction of a foundation's model size when applied to music information retrieval (MIR) tasks. Our research combines the Branchformer architecture with SummaryMixing, which were first applied in speech recognition

Research Question

How does COCO-DR's continuous contrastive pretraining on target corpora affect zero-shot accuracy on BEIR benchmark datasets compared to standard dense retrieval models like Contriever and DPR?

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