Scaling Dense Retrieval Pretraining with WebFAQ's 96M QA Pairs for Low-Resource Language Benchmarks

Abstract

Abstract: 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 Question

What is the impact of scaling dense retrieval pretraining with WebFAQ's 96 million QA pairs on the convergence speed and final MRR scores for low-resource language benchmarks?

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