# WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval

arXiv: https://arxiv.org/abs/2502.20936 · HTML: https://mlsanity.com/paper/2502.20936/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [MTEB: Massive Text Embedding Benchmark](https://mlsanity.com/paper/2210.07316/) (2022)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://mlsanity.com/paper/2407.19669/) (2024)
- [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://mlsanity.com/paper/2102.07033/) (2021)
- [MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering](https://mlsanity.com/paper/2007.15207/) (2020)
- [Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval](https://mlsanity.com/paper/2108.08787/) (2021)
- [CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training](https://mlsanity.com/paper/2110.07731/) (2021)
- [QUADRo: Dataset and Models for QUestion-Answer Database Retrieval](https://mlsanity.com/paper/2304.01003/) (2023)

## Built on by (most cited first, 10 of 10)

- [F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World](https://mlsanity.com/paper/2603.19223/) (2026)
- [MTEB-NL and E5-NL: Embedding Benchmark and Models for Dutch](https://mlsanity.com/paper/2509.12340/) (2025)
- [WebFAQ 2.0: A Multilingual QA Dataset with Mined Hard Negatives for Dense Retrieval](https://mlsanity.com/paper/2602.17327/) (2026)
- [Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering](https://mlsanity.com/paper/2604.24334/) (2026)
- [Nepali Passport Question Answering: A Low-Resource Dataset for Public Service Applications](https://mlsanity.com/paper/2603.13320/) (2026)
- [ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World](https://mlsanity.com/paper/2605.15081/) (2026)
- [Structure Retention in Embedding Spaces as a Predictor of Benchmark Performance](https://mlsanity.com/paper/2605.22202/) (2026)
- [SkMTEB: Slovak Massive Text Embedding Benchmark and Model Adaptation](https://mlsanity.com/paper/2606.13647/) (2026)
- [You Can Tell Who's Asking: What the Web's Questions Are Made Of, and Where They Come From](https://mlsanity.com/paper/2609.24106/) (2026)
- [ufakzeka-karar: An Open Turkish Typed-Decision Model with Order-Invariant Option Scoring](https://mlsanity.com/paper/2610.06744/) (2026)

## Abstract (arXiv)

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 multilingual dense retrieval models. To empirically confirm WebFAQ's efficacy, we use the collected QAs to fine-tune an in-domain pretrained XLM-RoBERTa model. Through this process of dataset-specific fine-tuning, the model achieves significant retrieval performance gains, which generalize - beyond WebFAQ - to other multilingual retrieval benchmarks evaluated in zero-shot setting. Last but not least, we utilize WebFAQ to construct a set of QA-aligned bilingual corpora spanning over 1000 language pairs using state-of-the-art bitext mining and automated LLM-assessed translation evaluation. Due to our advanced, automated method of bitext dataset generation, the resulting bilingual corpora demonstrate higher translation quality compared to similar datasets. WebFAQ and all associated resources are publicly available on GitHub and HuggingFace.