In this study, we focus on the efficacy of synthetic data generation and its impact on training models for multilingual retrieval tasks, demonstrating significant improvements.
We explore the question of whether contrastive pre-training is necessary for effective model performance in various tasks, revealing contexts where it may not be required.
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