Scalability of Synthetic Data Augmentation in Multilingual Dense Retrieval Performance on WebFAQ

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: How does the scalability of synthetic data augmentation affect the dense retrieval performance of multilingual models on WebFAQ, as measured by NDCG@10 when varying the amount of augmented data from 10% to 100% of the original dataset size?

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