We introduce WebFAQ 2.0, a new version of the WebFAQ dataset, containing 198 million FAQ-based natural question-answer pairs across 108 languages. Compared to the previous version, it significantly expands multilingual coverage and the number of bilingual aligned QA pairs to over 14.3M, making it the largest FAQ-based resource. Unlike the original release, WebFAQ 2.0 uses a novel data collection strategy that directly crawls and extracts relevant web content, resulting in a substantially more diverse and multilingual dataset with richer context through page titles and descriptions. In response to community feedback, we also release a hard negatives dataset for training dense retrievers, with 1.25M queries across 20 languages. These hard negatives were mined using a two-stage retrieval pipeline and include cross-encoder scores for 200 negatives per query. We further show how this resource enables two primary fine-tuning strategies for dense retrievers: Contrastive Learning with MultipleNegativesRanking loss, and Knowledge Distillation with MarginMSE loss. WebFAQ 2.0 is not a static resource but part of a long-term effort. Since late 2025, structured FAQs are being regularly released through the Open Web Index, enabling continuous expansion and refinement. We publish the datasets and training scripts to facilitate further research in multilingual and cross-lingual IR. The dataset itself and all related resources are publicly available on GitHub and HuggingFace.
Figures & tables
Figure 1. WebFAQ is a massive, publicly available repository of natural question-answer pairs. The second version focuses on QAs aligned across languages and hard negative mining. TODO
Language
%
Language
%
eng (English)
27.9
51.2
nld (Dutch)
4.3
2.8
spa (Spanish)
8.8
6.0
por (Portuguese)
4.0
1.7
deu (German)
7.3
6.9
ita (Italian)
3.5
2.7
fra (French)
6.5
4.8
pol (Polish)
2.5
1.7
rus (Russian)
4.4
3.8
Other
30.8
13.0
Table 1. Top 10 Languages in WebFAQ 2.0 (v1 in gray)
Topic
%
Traveling and Hospitality
59.2
34.1
Products and Commercial Services
18.9
19.8
Healthcare Services, Wellness and Lifestyle
5.6
13.0
Entertainment, Recreation, and Leisure
4.7
9.7
Banking, Financial Services, and Insurance
4.4
6.0
General Information and Other
4.4
3.9
Table 2. Distribution of topics (v1 in gray)
Question Type
Count
%
Not-a-Question
67,870,478
34,7
Factoid
65,601,236
33,6
Experience
22,649,722
11,6
Instruction
20,179,307
10,3
Evidence-Based
8,300,537
4,3
Reason
6,503,638
3,3
Table 3. Distribution of Question Types in WebFAQ 2.0
Language Pair
v1
v2
Marathi–Telugu
0
→
89,910
German–Spanish
19,739
→
80,623
Russian–Ukrainian
15,251
→
74,545
Italian–Portuguese
10,924
→
69,469
Indonesian–Korean
1,515
→
42,986
Table 4. Top language pairs for bilingual QA alignment.
WebFAQ
MIRACL (Hard Negatives)
Mr. Tydi
ara
eng
ind
jpn
kor
rus
ara
eng
ind
jpn
kor
rus
ara
eng
ind
jpn
kor
rus
BM25
29.7
24.4
36.1
29.0
29.6
21.2
53.2
31.6
51.6
44.7
37.6
29.8
43.4
19.9
50.5
25.0
24.8
29.7
Base
61.0
49.7
74.6
56.5
73.2
50.1
36.1
30.6
28.1
27.6
39.3
29.6
30.5
18.1
33.2
16.0
27.7
22.5
MNR (RN)
74.3
60.0
81.9
69.8
81.3
62.0
50.5
37.2
36.1
39.8
42.4
39.6
44.7
29.5
47.7
33.5
34.7
30.8
MNR Top4
71.8
48.7
78.8
63.6
81.4
51.4
40.3
23.8
26.6
31.4
35.3
27.6
34.1
13.0
36.9
25.1
26.9
19.2
MNR Den.
74.8
57.8
82.2
69.6
83.1
59.9
46.4
29.7
31.7
36.6
39.0
34.8
39.5
20.1
44.7
30.1
32.6
26.9
Table 5. Comparing retrieval performance on 3 multilingual datasets using NDCG@10 in %, including BM25 and XLM-RoBERTa-base with in-domain pre-training on MS MARCO ( Base ) as references. MNR (RN) represent the Base model fine-tuned on WebFAQ random negatives. The three bottom rows represent training with hard negatives. The top scores are marked in bold, while the second-best are underlined. Using the reranker scores directly in the loss function yields the best results ( M-MSE ).
Manning College of Information & Computer Sciences, University of Massachusetts Amherst · Institute for the Study of the Ancient World, New York University