Diminishing Returns in Cross-Lingual Zero-Shot Dense Retrieval for Low-Resource Languages via WebFAQ Scaling

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

Does scaling dense retrieval pre-training with WebFAQ's monolingual non-English benchmarks yield diminishing returns in cross-lingual zero-shot MRR@10 for languages with fewer than 100k training samples?

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Automated triage signals derived from public fields; not human peer review or independent validation.

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Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance