Research Insights – Learning to Retrieve Passages without Supervision

The Learning to Retrieve Passages without Supervision paper by Ori Ram et al. explores Self-Supervised Learning as an alternative route to training new models without human labeling. They introduce Span-based Unsupervised Dense Retriever (Spider), a recent breakthrough for Self-Supervised representation learning applied to text retrieval in search. Spider's results show that it achieves similar performance to Supervised models on unseen data distributions, demonstrating strong Zero-Shot Generalization capabilities. The authors further illustrate how we can target Spider's performance to a particular data distribution with Transfer Learning, requiring only 128 labeled examples. This research opens up new possibilities for training custom retrieval models without the need for large labeled datasets.

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