Zero-Shot Cross-Lingual Transfer Degradation in Multilingual Dense Retrievers on Low-Resource WebFAQ Benchmarks

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 zero-shot cross-lingual transfer performance of multilingual dense retrievers degrade on low-resource languages in the WebFAQ benchmark compared to fine-tuned monolingual models?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.7/10.