Scaling Typoed Positives in Multi-Positive Contrastive Learning for Robust Dense Retrieval

Dense retrieval has become the new paradigm in passage retrieval. Despite its effectiveness on typo-free queries, it is not robust when dealing with queries that contain typos. Current works on improving the typo-robustness of dense retrievers combine (i) data augmentation to obtain the typoed queries during training time with (ii) additional robustifying subtasks that aim to align the original, typo-free queries with their typoed variants. Even though multiple typoed variants are available as positive samples per query, some methods assume a single positive sample and a set of negative ones p

Research goal: What is the impact of scaling the number of typoed positive examples in multi-positive contrastive learning on the robustness of dense retrieval models against adversarial character perturbations, as measured by the recall@100 metric on the Robust04 benchmark?

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