Impact of Robustness Training on Zero-Shot Cross-Domain Generalization in Dense Retrieval Models

Dense retrieval is becoming one of the standard approaches for document and passage ranking. The dual-encoder architecture is widely adopted for scoring question-passage pairs due to its efficiency and high performance. Typically, dense retrieval models are evaluated on clean and curated datasets. However, when deployed in real-life applications, these models encounter noisy user-generated text. That said, the performance of state-of-the-art dense retrievers can substantially deteriorate when exposed to noisy text. In this work, we study the robustness of dense retrievers against typos in the

Research goal: How does the inclusion of robustness training against misspellings impact the zero-shot cross-domain generalization performance of dense retrieval models on clean datasets like MS MARCO and BEIR?

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