---
title: "Natural language inference (SNLI, MultiNLI)"
description: "Natural language inference (SNLI, MultiNLI): compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends."
url: https://scholar4ai.com/datasets/natural-language-inference-snli-multinli
markdown_url: https://scholar4ai.com/datasets/natural-language-inference-snli-multinli.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Natural language inference (SNLI, MultiNLI): 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)
---

# Natural language inference (SNLI, MultiNLI)

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/5.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: SNLI, MultiNLI, MNLI

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 |
|---|---|---|---|
| SNLI | Three-way natural-language inference: entailment, contradiction, neutral. Evaluation: Accuracy; inspect label artifacts and distinguish caption-derived sentences from general-domain text. | [Official source / documentation](https://nlp.stanford.edu/projects/snli/) Download SNLI 1.0 JSONL/TSV from Stanford; preserve the official train/dev/test split. | CC BY-SA 4.0 as stated by the creators; attribution and share-alike conditions apply. |
| MultiNLI | Three-way inference across multiple spoken and written genres. Evaluation: Report matched and mismatched accuracy separately; disclose whether SNLI was used in training. | [Official source / documentation](https://cims.nyu.edu/~sbowman/multinli/) [Source-text access](https://cims.nyu.edu/~sbowman/multinli/paper.pdf) Download the NYU corpus; matched and mismatched sets test within-genre and cross-genre generalization. | Most content uses the permissive OANC terms. Fiction sources include CC BY 3.0, CC BY-SA 3.0 and US-public-domain texts; follow the source-specific notices rather than assuming one corpus-wide license. |

Guide updated 2026-11-04.

## Key findings

- From Nov 2023 to Oct 2026, 87 archived papers mentioned Natural language inference (SNLI, MultiNLI) in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Nov 2023 (6).
- Natural language inference (SNLI, MultiNLI) was mentioned in 1 paper in the last 30 days (2026-10-07 to 2026-11-05), ranking #165 of the 246 datasets mentioned.
- By journal category: AI (1).
- Most of them appeared in Machine Learning (1).
- From 2017 to 2026 (so far), Natural language inference (SNLI, MultiNLI) was mentioned in 94 papers in the tracked journals, most of them in AI (76), economics (7) and operations research (3).
- 28 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 (4), Nanyang Technological University (4) and Hong Kong Polytechnic University (3).
- The journals publishing most of them were Knowledge-Based Systems (10), Neural Networks (8) and IEEE Transactions on Pattern Analysis and Machine Intelligence (6).
- The most-cited of them is “Unifying Large Language Models and Knowledge Graphs: A Roadmap” (IEEE Transactions on Knowledge and Data Engineering, 2024), with 1,381 citations.
- Papers use Natural language inference (SNLI, MultiNLI) most often with Hugging Face Hub (29), Wikipedia (29) and Stanford Sentiment Treebank (27); it has 30 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 |
|---|---:|---:|---:|---:|
|  | 31 | 31 | 25 | 87 |

## 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 | 0 | 3 | 9 | 27 | 21 | 15 |
| [Economics](https://scholar4ai.com/categories/economics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 2 |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| [Management](https://scholar4ai.com/categories/management.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| [Tourism and Hospitality](https://scholar4ai.com/categories/tourism-and-hospitality.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| [Finance](https://scholar4ai.com/categories/finance.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| [Higher Education and Research](https://scholar4ai.com/categories/higher-education-and-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| All categories | 0 | 0 | 0 | 1 | 0 | 3 | 10 | 31 | 28 | 21 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 10 |
| 2 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 8 |
| 3 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 6 |
| 4 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 6 |
| 5 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 6 |
| 6 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 5 |
| 7 | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 4 |
| 8 | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | 4 |
| 9 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 3 |
| 10 | [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | 3 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 4 |
| 2 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 4 |
| 3 | [Hong Kong Polytechnic University](https://scholar4ai.com/institutions/hong-kong-polytechnic-university.md) | 3 |
| 4 | [The University of Sydney](https://scholar4ai.com/institutions/the-university-of-sydney.md) | 3 |
| 5 | Universidad Politécnica de Madrid | 3 |
| 6 | [Alibaba Group (China)](https://scholar4ai.com/companies/alibaba-group-china.md) | 2 |
| 7 | [Anhui University](https://scholar4ai.com/institutions/anhui-university.md) | 2 |
| 8 | [Baidu (China)](https://scholar4ai.com/companies/baidu-china.md) | 2 |
| 9 | [Beijing University of Technology](https://scholar4ai.com/institutions/beijing-university-of-technology.md) | 2 |
| 10 | [Chinese University of Hong Kong](https://scholar4ai.com/institutions/chinese-university-of-hong-kong.md) | 2 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 33 |
| 2 | Australia | 11 |
| 3 | United Kingdom | 10 |
| 4 | Italy | 10 |
| 5 | Spain | 7 |
| 6 | Germany | 6 |
| 7 | Hong Kong | 6 |
| 8 | Singapore | 6 |
| 9 | United States | 6 |
| 10 | France | 4 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 28 |
| 2 | [BERT](https://scholar4ai.com/keywords/bert.md) | 12 |
| 3 | natural language inference | 10 |
| 4 | [sentiment analysis](https://scholar4ai.com/keywords/sentiment-analysis.md) | 8 |
| 5 | parameter-efficient fine-tuning | 7 |
| 6 | [transformer models](https://scholar4ai.com/keywords/transformer-models.md) | 7 |
| 7 | natural language processing | 6 |
| 8 | fine-tuning | 5 |
| 9 | [knowledge graphs](https://scholar4ai.com/keywords/knowledge-graphs.md) | 5 |
| 10 | language models | 5 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 1,381 | 40.8 | [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://doi.org/10.1109/tkde.2024.3352100) | [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | 2024-01-10 | Shirui Pan, Linhao Luo, Yufei Wang et al. |
| 2 | 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 |
| 3 | 495 | 14.0 | [A comprehensive survey on applications of transformers for deep learning tasks](https://doi.org/10.1016/j.eswa.2023.122666) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 2023-11-22 | Saidul Islam, Hanae Elmekki, Ahmed Elsebai et al. |
| 4 | 427 | 125.0 | [Can ChatGPT forecast stock price movements? Return predictability and large language models](https://doi.org/10.1016/j.jfineco.2026.104335) | [Journal of Financial Economics](https://scholar4ai.com/journals/journal-of-financial-economics.md) | 2026-07-24 | Alejandro Lopez-Lira, Yuehua Tang |
| 5 | 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. |

## Used together with

Papers use Natural language inference (SNLI, MultiNLI) most often with Hugging Face Hub (29), Wikipedia (29) and Stanford Sentiment Treebank (27); it has 30 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

*What papers use Natural language inference (SNLI, MultiNLI) 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 | 29 | 31.6 |
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 29 | 14.5 |
| [Stanford Sentiment Treebank](https://scholar4ai.com/datasets/stanford-sentiment-treebank.md) | Dataset | 27 | 236.9 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | Dataset | 27 | 3.1 |
| [GLUE / SuperGLUE](https://scholar4ai.com/datasets/glue-superglue.md) | Dataset | 24 | 113.1 |
| [IMDb](https://scholar4ai.com/datasets/imdb.md) | Dataset | 19 | 41.1 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 18 | 3.9 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | Dataset | 14 | 5.1 |
| [CoNLL](https://scholar4ai.com/datasets/conll.md) | Dataset | 11 | 44.1 |
| [Facebook](https://scholar4ai.com/datasets/facebook.md) | Dataset | 11 | 1.9 |
| [AG News / 20 Newsgroups](https://scholar4ai.com/datasets/ag-news-20-newsgroups.md) | Dataset | 10 | 26.0 |
| [MMLU](https://scholar4ai.com/datasets/mmlu.md) | Dataset | 9 | 58.0 |

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

## Papers

*Papers mentioning Natural language inference (SNLI, MultiNLI) in the last 30 days, newest first.*

| Date | Title | Journal | Authors | Affiliations |
|---|---|---|---|---|
| 2026-10-20 | [Weighted UnLearning from Experience for Improved Spurious Correlation Robustness](https://doi.org/10.1007/s10994-026-07164-1) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Jeff Mitchell, Jesus Martinez-del-Rincon, Niall McLaughlin | Queen's University Belfast |

## Cite this page

Scholar4AI. (2026). Natural language inference (SNLI, MultiNLI). Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/natural-language-inference-snli-multinli

```bibtex
@misc{scholar4ai-datasets-natural-language-inference-snli-multinli,
  author = {{Scholar4AI}},
  title = {Natural language inference (SNLI, MultiNLI)},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/natural-language-inference-snli-multinli}},
  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/).