Domain-Specific Fine-Tuning Effects on Multilingual Dense Retrieval Accuracy in Low-Resource 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: What is the impact of domain-specific fine-tuning on the dense retrieval accuracy of multilingual models for low-resource languages in WebFAQ, as measured by NDCG@10 when compared to general-domain pretraining?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.8/10.