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

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: 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?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.7/10.