ColBERT-Zero: To Pre-train Or Not To Pre-train ColBERT models?

ColBERT-Zero introduces a novel training approach for ColBERT models, emphasizing the importance of contrastive pre-training in the multi-vector setting, which traditionally has been underutilized compared to dense models. By leveraging PyLate for efficient large-scale pre-training, the study demonstrates that ColBERT-Zero can outperform existing state-of-the-art models like GTE-ModernColBERT using only public datasets. The research highlights that while knowledge distillation remains a key component, incorporating a supervised contrastive step before distillation significantly enhances efficiency and performance, nearly matching full pre-training outcomes at a fraction of the cost. Additionally, maintaining prompt alignment between pre-training and fine-tuning phases is crucial to maximize performance, suggesting that prompts might serve as implicit query expansion tools. The findings underscore the potential of public data to rival proprietary models when multi-vector objectives are prioritized, and they provide insights into optimizing training pipelines by integrating supervised steps and ensuring prompt consistency.