Impact of Fine-Tuning Dense Retrieval Models on Native Multilingual WebFAQ Data for Zero-Shot Cross-Lingual Accuracy on XQuAD

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 fine-tuning dense retrieval models on native multilingual WebFAQ data impact zero-shot cross-lingual retrieval accuracy on XQuAD compared to models trained on translated English-only datasets?

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