---
title: "GLUE / SuperGLUE: tasks, downloads and usage conditions"
description: "GLUE / SuperGLUE: compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends."
url: https://scholar4ai.com/datasets/glue-superglue
markdown_url: https://scholar4ai.com/datasets/glue-superglue.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "GLUE / SuperGLUE: 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)
---

# GLUE / SuperGLUE

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/3.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: GLUE benchmark, General Language Understanding Evaluation, SuperGLUE, GLUE

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.

*NLP dataset comparison: tasks, access and usage conditions*

| Dataset / version | Task and evaluation | Access and preparation | Usage conditions |
|---|---|---|---|
| GLUE | A suite spanning sentiment, acceptability, paraphrase, similarity and inference. Evaluation: Report the prescribed score for each task and suite aggregate; STS-B, MRPC and other tasks use different metrics. | [Official source / documentation](https://gluebenchmark.com/) Use official task downloads and splits; several test labels are hidden for benchmark evaluation. | Component datasets retain their original terms; there is no single reuse license implied by a GLUE score. |
| SuperGLUE | A separate suite emphasizing reading comprehension, reasoning and difficult language understanding. Evaluation: Use each task’s specified metrics and the official aggregate; it is not directly interchangeable with GLUE. | [Official source / documentation](https://super.gluebenchmark.com/) Use official task-specific downloads; keep training, validation and submitted test evaluation distinct. | Review each component’s license and source-text conditions separately, following the benchmark FAQ and original releases. |

Guide updated 2026-11-04.

## Key findings

- From Nov 2023 to Oct 2026, 98 archived papers mentioned GLUE / SuperGLUE in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Dec 2024 (7).
- No paper of the tracked journals mentioned GLUE / SuperGLUE in the last 30 days (2026-10-07 to 2026-11-05).
- From 2017 to 2026 (so far), GLUE / SuperGLUE was mentioned in 108 papers in the tracked journals, most of them in AI (100), information systems (4) and operations research (4).
- 32 of them were published in 2025, against 0 in 2017.
- Since 2023, the institutions with the most of these papers were Chinese Academy of Sciences (10), Institute of Automation (6) and Czech Technical University in Prague (3).
- The journals publishing most of them were Neural Networks (12), IEEE Transactions on Pattern Analysis and Machine Intelligence (11) and Knowledge-Based Systems (9).
- The most-cited of them is “A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations” (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024), with 512 citations.
- Papers use GLUE / SuperGLUE most often with Hugging Face Hub (26), Natural language inference (SNLI, MultiNLI) (24) and Stanford Sentiment Treebank (19); it has 28 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 |
|---|---:|---:|---:|---:|
|  | 32 | 33 | 33 | 98 |

## 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 | 0 | 1 | 1 | 1 | 12 | 33 | 29 | 23 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 1 |
| All categories | 0 | 0 | 0 | 1 | 1 | 1 | 12 | 35 | 32 | 26 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 12 |
| 2 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 11 |
| 3 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 9 |
| 4 | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 8 |
| 5 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 6 |
| 6 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 6 |
| 7 | [Language Resources and Evaluation](https://scholar4ai.com/journals/language-resources-and-evaluation.md) | 5 |
| 8 | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 4 |
| 9 | [Computer Speech & Language](https://scholar4ai.com/journals/computer-speech-language.md) | 4 |
| 10 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 4 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 10 |
| 2 | [Institute of Automation](https://scholar4ai.com/institutions/institute-of-automation.md) | 6 |
| 3 | Czech Technical University in Prague | 3 |
| 4 | [Korea Advanced Institute of Science and Technology](https://scholar4ai.com/institutions/korea-advanced-institute-of-science-and-technology.md) | 3 |
| 5 | [Meta (United States)](https://scholar4ai.com/companies/meta-united-states.md) | 3 |
| 6 | [National University of Defense Technology](https://scholar4ai.com/institutions/national-university-of-defense-technology.md) | 3 |
| 7 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 3 |
| 8 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 3 |
| 9 | [Technical University of Munich](https://scholar4ai.com/institutions/technical-university-of-munich.md) | 3 |
| 10 | [Tencent (China)](https://scholar4ai.com/companies/tencent-china.md) | 3 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 47 |
| 2 | United States | 12 |
| 3 | Australia | 10 |
| 4 | United Kingdom | 10 |
| 5 | Italy | 9 |
| 6 | Germany | 8 |
| 7 | South Korea | 7 |
| 8 | Switzerland | 4 |
| 9 | Hong Kong | 4 |
| 10 | India | 4 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 26 |
| 2 | [BERT](https://scholar4ai.com/keywords/bert.md) | 17 |
| 3 | GLUE benchmark | 14 |
| 4 | pre-trained language models | 11 |
| 5 | [transformer models](https://scholar4ai.com/keywords/transformer-models.md) | 10 |
| 6 | parameter-efficient fine-tuning | 8 |
| 7 | [self-supervised learning](https://scholar4ai.com/keywords/self-supervised-learning.md) | 7 |
| 8 | low-rank adaptation | 6 |
| 9 | model compression | 6 |
| 10 | natural language processing | 6 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 512 | 19.3 | [A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations](https://doi.org/10.1109/tpami.2024.3447085) | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 2024-08-21 | Hongrong Cheng, Miao Zhang, Qinfeng Shi |
| 2 | 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. |
| 3 | 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. |
| 4 | 188 | 7.1 | [Factuality challenges in the era of large language models and opportunities for fact-checking](https://doi.org/10.1038/s42256-024-00881-z) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 2024-08-22 | Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha et al. |
| 5 | 136 | 6.7 | [Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Information Systems Research](https://doi.org/10.1007/s10796-025-10581-7) | [Information Systems Frontiers](https://scholar4ai.com/journals/information-systems-frontiers.md) | 2025-02-24 | Veda C. Storey, Wei Thoo Yue, J. Leon Zhao et al. |

## Used together with

Papers use GLUE / SuperGLUE most often with Hugging Face Hub (26), Natural language inference (SNLI, MultiNLI) (24) and Stanford Sentiment Treebank (19); it has 28 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

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

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [Hugging Face Hub](https://scholar4ai.com/datasets/hugging-face-hub.md) | Dataset | 26 | 24.7 |
| [Natural language inference (SNLI, MultiNLI)](https://scholar4ai.com/datasets/natural-language-inference-snli-multinli.md) | Dataset | 24 | 113.1 |
| [Stanford Sentiment Treebank](https://scholar4ai.com/datasets/stanford-sentiment-treebank.md) | Dataset | 19 | 145.1 |
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 18 | 7.8 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 16 | 3.0 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | Dataset | 14 | 4.5 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | Dataset | 12 | 1.2 |
| [CoNLL](https://scholar4ai.com/datasets/conll.md) | Dataset | 11 | 38.4 |
| [BIG-bench](https://scholar4ai.com/datasets/big-bench.md) | Dataset | 8 | 114.3 |
| [IMDb](https://scholar4ai.com/datasets/imdb.md) | Dataset | 8 | 15.1 |
| [MMLU](https://scholar4ai.com/datasets/mmlu.md) | Dataset | 8 | 44.9 |
| [Pre-training corpora (Common Crawl, C4, The Pile)](https://scholar4ai.com/datasets/pre-training-corpora-common-crawl-c4-the-pile.md) | Dataset | 8 | 32.2 |

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

## Cite this page

Scholar4AI. (2026). GLUE / SuperGLUE. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/glue-superglue

```bibtex
@misc{scholar4ai-datasets-glue-superglue,
  author = {{Scholar4AI}},
  title = {GLUE / SuperGLUE},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/glue-superglue}},
  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/).