---
title: "SQuAD: tasks, downloads and usage conditions"
description: "SQuAD: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends."
url: https://scholar4ai.com/datasets/squad
markdown_url: https://scholar4ai.com/datasets/squad.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "SQuAD: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends. (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# SQuAD

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/6.md)

Corpora and benchmarks for language understanding, question answering and text classification.

- data category: Text and NLP benchmarks
- 0 papers in the last 7 days
- Names searched for: Stanford Question Answering Dataset, SQuAD

Most-used data: https://scholar4ai.com/rankings/datasets.md · All Text and NLP benchmarks datasets: https://scholar4ai.com/datasets/text-and-nlp-benchmarks.md

## Tasks, access and usage conditions

Mention counts below measure name matches in tracked papers, not benchmark quality or downloads. A family count can cover multiple datasets; it is not a separate count for each comparison row. Choose a task and version first, preserve evaluation splits, and follow the linked release terms. The site's content license does not replace the datasets' own terms.

*Dataset comparison: tasks, access and usage conditions*

| Dataset / version | Task and evaluation | Access and preparation | Usage conditions |
|---|---|---|---|
| SQuAD 1.1 / 2.0 | Extractive reading comprehension; v2 adds questions unanswerable from the supplied passage. Evaluation: Exact match and token F1; for v2 report the no-answer decision setup. Supplied-passage QA is different from corpus retrieval. | [Official source / documentation](https://rajpurkar.github.io/SQuAD-explorer/) Download versioned train/dev JSON and evaluation script; choose v1.1 or v2.0 explicitly. | CC BY-SA 4.0 in the official project; retain attribution and applicable share-alike notices. |

Guide updated 2026-11-05.

## Key findings

- From Nov 2023 to Oct 2026, 15 archived papers mentioned SQuAD in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Jul 2026 (3).
- No paper of the tracked journals mentioned SQuAD in the last 30 days (2026-10-07 to 2026-11-05).
- From 2017 to 2026 (so far), SQuAD was mentioned in 26 papers in the tracked journals, most of them in AI (22), information systems (2) and management (1).
- 4 of them were published in 2025, against 0 in 2017.
- Since 2023, the institutions with the most of these papers were American University (1), Anhui University (1) and BI Norwegian Business School (1).
- The journals publishing most of them were Artificial Intelligence Review (3), Knowledge and Information Systems (2) and Technological Forecasting and Social Change (1).
- The most-cited of them is “A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges” (Artificial Intelligence Review, 2025), with 275 citations.
- Papers use SQuAD most often with Wikipedia (7), GLUE / SuperGLUE (5) and Retrieval and QA (MS MARCO, Natural Questions, HotpotQA) (5); it has 5 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

## Mentions over 36 months

*Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)*

| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---:|---:|---:|---:|
|  | 7 | 4 | 4 | 15 |

## Papers per year

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 0 | 0 | 1 | 1 | 5 | 3 | 1 | 6 | 3 | 2 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| [Management](https://scholar4ai.com/categories/management.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| [Economics](https://scholar4ai.com/categories/economics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| All categories | 0 | 0 | 1 | 1 | 6 | 3 | 1 | 7 | 4 | 3 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 3 |
| 2 | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 2 |
| 3 | [Technological Forecasting and Social Change](https://scholar4ai.com/journals/technological-forecasting-and-social-change.md) | 1 |
| 4 | [Data & Knowledge Engineering](https://scholar4ai.com/journals/data-knowledge-engineering.md) | 1 |
| 5 | [Journal of Econometrics](https://scholar4ai.com/journals/journal-of-econometrics.md) | 1 |
| 6 | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 1 |
| 7 | [Computer Speech & Language](https://scholar4ai.com/journals/computer-speech-language.md) | 1 |
| 8 | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 1 |
| 9 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| 10 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 1 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | American University | 1 |
| 2 | [Anhui University](https://scholar4ai.com/institutions/anhui-university.md) | 1 |
| 3 | [BI Norwegian Business School](https://scholar4ai.com/institutions/bi-norwegian-business-school.md) | 1 |
| 4 | [Erasmus University Rotterdam](https://scholar4ai.com/institutions/erasmus-university-rotterdam.md) | 1 |
| 5 | Hasso Plattner Institute | 1 |
| 6 | [Huawei Technologies (China)](https://scholar4ai.com/companies/huawei-technologies-china.md) | 1 |
| 7 | Kadir Has University | 1 |
| 8 | LG Chem (South Korea) | 1 |
| 9 | [Lanzhou University](https://scholar4ai.com/institutions/lanzhou-university.md) | 1 |
| 10 | [Nanjing University of Science and Technology](https://scholar4ai.com/institutions/nanjing-university-of-science-and-technology.md) | 1 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 3 |
| 2 | Canada | 2 |
| 3 | Germany | 2 |
| 4 | South Korea | 2 |
| 5 | Australia | 1 |
| 6 | Brazil | 1 |
| 7 | Spain | 1 |
| 8 | United Kingdom | 1 |
| 9 | Netherlands | 1 |
| 10 | Norway | 1 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [BERT](https://scholar4ai.com/keywords/bert.md) | 5 |
| 2 | SQuAD | 3 |
| 3 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 3 |
| 4 | natural language processing | 3 |
| 5 | [transformer models](https://scholar4ai.com/keywords/transformer-models.md) | 3 |
| 6 | large language model compression | 2 |
| 7 | pre-trained language models | 2 |
| 8 | question answering | 2 |
| 9 | question generation | 2 |
| 10 | [ChatGPT](https://scholar4ai.com/keywords/chatgpt.md) | 1 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 275 | 14.9 | [A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges](https://doi.org/10.1007/s10462-025-11223-9) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2025-04-23 | Jongseon Kim, Hyungjoon Kim, HyunGi Kim et al. |
| 2 | 271 | 10.4 | [A comprehensive review of model compression techniques in machine learning](https://doi.org/10.1007/s10489-024-05747-w) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 2024-09-02 | Pierre V. Dantas, Waldir Sabino da Silva, Lucas Carvalho Cordeiro et al. |
| 3 | 40 | 1.1 | [Revealing the technology development of natural language processing: A Scientific entity-centric perspective](https://doi.org/10.1016/j.ipm.2023.103574) | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 2023-11-14 | Heng Zhang, Chengzhi Zhang, Yuzhuo Wang |
| 4 | 25 | 0.4 | [Natural language based analysis of SQuAD: An analytical approach for BERT](https://doi.org/10.1016/j.eswa.2022.116592) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2022-01-31 | Zekeriya Anıl Güven, Murat Osman Ünalır |
| 5 | 24 | 0.4 | [APER: AdaPtive Evidence-driven Reasoning Network for machine reading comprehension with unanswerable questions](https://doi.org/10.1016/j.knosys.2021.107364) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 2021-08-08 | Wei Peng, Yue Hu, Jing Yu et al. |

## Used together with

Papers use SQuAD most often with Wikipedia (7), GLUE / SuperGLUE (5) and Retrieval and QA (MS MARCO, Natural Questions, HotpotQA) (5); it has 5 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

*What papers use SQuAD together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 7 | 12.7 |
| [GLUE / SuperGLUE](https://scholar4ai.com/datasets/glue-superglue.md) | Dataset | 5 | 85.2 |
| [Retrieval and QA (MS MARCO, Natural Questions, HotpotQA)](https://scholar4ai.com/datasets/retrieval-and-qa-ms-marco-natural-questions-hotpotqa.md) | Dataset | 5 | 115.0 |
| [Natural language inference (SNLI, MultiNLI)](https://scholar4ai.com/datasets/natural-language-inference-snli-multinli.md) | Dataset | 3 | 58.7 |
| [Stanford Sentiment Treebank](https://scholar4ai.com/datasets/stanford-sentiment-treebank.md) | Dataset | 3 | 95.2 |

The whole network: https://scholar4ai.com/code/network.md

## Cite this page

Scholar4AI. (2026). SQuAD. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/squad

```bibtex
@misc{scholar4ai-datasets-squad,
  author = {{Scholar4AI}},
  title = {SQuAD},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/squad}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).