Embedding And Clustering Your Data Can Improve Contrastive Pretraining
<|reference_start|>Embedding And Clustering Your Data Can Improve Contrastive Pretraining: Recent studies of large-scale contrastive pretraining in the text embedding domain show that using single-source minibatches, rather than mixed-source minibatches, can substantially improve overall model accuracy. In this work, we explore extending training data stratification beyond source granularity by leveraging a pretrained text embedding model and the classic k-means clustering algorithm to further split training data apart by the semantic clusters within each source. Experimentally, we observe a notable increase in NDCG@10 when pretraining a BERT-based text embedding model on query-passage pairs from the MSMARCO passage retrieval dataset. Additionally, we conceptually connect our clustering approach to both the Topic Aware Sampling (TAS) aspect of the TAS-B methodology and the nearest-neighbor-based hard-negative mining aspect of the ANCE methodology and discuss how this unified view motivates future lines of research on the organization of contrastive pretraining data.<|reference_end|>
@article{merrick2024embedding,
    title={Embedding And Clustering Your Data Can Improve Contrastive Pretraining},
    author={Luke Merrick},
    journal={arXiv preprint arXiv:2407.18887},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18887},
    primaryClass={cs.LG cs.CL}
}