Misspelling Robustness Training and Zero-Shot Cross-Domain Performance in Dense Retrieval Models

Abstract

Abstract: Prevailing research practice today often relies on training dense retrievers on existing large datasets such as MSMARCO and then experimenting with ways to improve zero-shot generalization capabilities to unseen domains. While prior work has tackled this challenge through resource-intensive steps such as data augmentation, architectural modifications, increasing model size, or even further base model pretraining, comparatively little investigation has examined whether the training procedures themselves can be improved to yield better generalization capabilities in the resulting models. In this

Research Question

What is the impact of misspelling robustness training on the zero-shot cross-domain performance of dense retrieval models evaluated on diverse benchmark suites?

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