Multitask Fine-Tuning for Robust Dense Retrieval Against Query Perturbations

We present WebFAQ, a large-scale collection of open-domain question answering datasets derived from FAQ-style schema.org annotations. In total, the data collection consists of 96 million natural question-answer (QA) pairs across 75 languages, including 47 million (49\%) non-English samples. WebFAQ further serves as the foundation for 20 monolingual retrieval benchmarks with a total size of 11.2 million QA pairs (5.9 million non-English). These datasets are carefully curated through refined filtering and near-duplicate detection, yielding high-quality resources for training and evaluating multil

Research goal: Can multitask fine-tuning with WebFAQ's monolingual retrieval benchmarks improve the robustness of dense retrievers against adversarial perturbations in the query text, as evaluated by accuracy drops on perturbed XQuAD samples?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.