---
title: "Retrieval and QA (MS MARCO, Natural Questions, HotpotQA)"
description: "Retrieval and QA (MS MARCO, Natural Questions, HotpotQA): compare tasks, official access, usage conditions and evaluation. Includes research-paper mention counts and trends."
url: https://scholar4ai.com/datasets/retrieval-and-qa-ms-marco-natural-questions-hotpotqa
markdown_url: https://scholar4ai.com/datasets/retrieval-and-qa-ms-marco-natural-questions-hotpotqa.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Retrieval and QA (MS MARCO, Natural Questions, HotpotQA): 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)
---

# Retrieval and QA (MS MARCO, Natural Questions, HotpotQA)

[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: MS MARCO, MSMARCO, Natural Questions dataset, Natural Questions benchmark, HotpotQA

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 |
|---|---|---|---|
| MS MARCO | Passage/document retrieval, ranking and question answering using search questions. Evaluation: Use task-specific ranking or QA metrics and judgments; record corpus version and candidate-generation setup. | [Official source / documentation](https://microsoft.github.io/msmarco/) Choose the specific task/release on the official page and accept its terms; ranking and QA collections differ. | Official terms restrict the datasets to non-commercial research; acceptance is required to access downloads. Source documents can carry separate rights. |
| Natural Questions | Questions from search with long/short answers annotated on Wikipedia pages. Evaluation: Official long-/short-answer metrics for full NQ; exact match for an open-domain derivative is a different task. | [Official source / documentation](https://github.com/google-research-datasets/natural-questions) Use the official release links and distinguish full NQ from simplified/open-domain derivatives. | The official repository is Apache-2.0 licensed; preserve the underlying Wikipedia content attribution and applicable terms as well. |
| HotpotQA | Multi-hop question answering with supporting-fact annotations. Evaluation: Answer EM/F1, supporting-fact scores and joint scores; retrieval difficulty differs sharply between the two settings. | [Official source / documentation](https://hotpotqa.github.io/) Download the official JSON release; choose distractor or fullwiki settings. | CC BY-SA 4.0 as stated by the creators; retain attribution and share-alike notices. |

Guide updated 2026-11-04.

## Key findings

- From Nov 2023 to Oct 2026, 68 archived papers mentioned Retrieval and QA (MS MARCO, Natural Questions, HotpotQA) in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Feb 2025 (5).
- No paper of the tracked journals mentioned Retrieval and QA (MS MARCO, Natural Questions, HotpotQA) in the last 30 days (2026-10-07 to 2026-11-05).
- From 2017 to 2026 (so far), Retrieval and QA (MS MARCO, Natural Questions, HotpotQA) was mentioned in 80 papers in the tracked journals, most of them in AI (75), information systems (3) and finance (1).
- 31 of them were published in 2025, against 0 in 2017.
- Since 2023, the institutions with the most of these papers were Tsinghua University (3), University of Isfahan (3) and Wuhan University (3).
- The journals publishing most of them were Knowledge-Based Systems (10), Artificial Intelligence Review (9) and Information Processing & Management (8).
- The most-cited of them is “Large language models in medical and healthcare fields: applications, advances, and challenges” (Artificial Intelligence Review, 2024), with 247 citations.
- Papers use Retrieval and QA (MS MARCO, Natural Questions, HotpotQA) most often with Wikipedia (30), Hugging Face Hub (17) and Twitter / X (9); it has 20 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 |
|---|---:|---:|---:|---:|
|  | 11 | 27 | 30 | 68 |

## 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 | 0 | 0 | 6 | 5 | 12 | 31 | 21 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 |
| [Finance](https://scholar4ai.com/categories/finance.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| [Economics](https://scholar4ai.com/categories/economics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| All categories | 0 | 0 | 0 | 0 | 1 | 6 | 6 | 13 | 31 | 23 |

## 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 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 9 |
| 3 | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 8 |
| 4 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 5 |
| 5 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 5 |
| 6 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 5 |
| 7 | [IEEE Transactions on Knowledge and Data Engineering](https://scholar4ai.com/journals/ieee-transactions-on-knowledge-and-data-engineering.md) | 5 |
| 8 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 3 |
| 9 | [Language Resources and Evaluation](https://scholar4ai.com/journals/language-resources-and-evaluation.md) | 3 |
| 10 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 3 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 3 |
| 2 | University of Isfahan | 3 |
| 3 | [Wuhan University](https://scholar4ai.com/institutions/wuhan-university.md) | 3 |
| 4 | [Beijing Institute of Technology](https://scholar4ai.com/institutions/beijing-institute-of-technology.md) | 2 |
| 5 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 2 |
| 6 | [Eindhoven University of Technology](https://scholar4ai.com/institutions/eindhoven-university-of-technology.md) | 2 |
| 7 | Loughborough University | 2 |
| 8 | [Southeast University](https://scholar4ai.com/institutions/southeast-university.md) | 2 |
| 9 | [Southern University of Science and Technology](https://scholar4ai.com/institutions/southern-university-of-science-and-technology.md) | 2 |
| 10 | Toronto Metropolitan University | 2 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 28 |
| 2 | United Kingdom | 11 |
| 3 | United States | 7 |
| 4 | Australia | 6 |
| 5 | Italy | 6 |
| 6 | Canada | 5 |
| 7 | Hong Kong | 5 |
| 8 | Iran | 5 |
| 9 | Germany | 4 |
| 10 | Spain | 3 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 32 |
| 2 | HotpotQA | 12 |
| 3 | retrieval-augmented generation | 11 |
| 4 | MS MARCO | 6 |
| 5 | dense retrieval | 6 |
| 6 | hallucination mitigation | 6 |
| 7 | information retrieval | 5 |
| 8 | [knowledge graphs](https://scholar4ai.com/keywords/knowledge-graphs.md) | 5 |
| 9 | multi-hop question answering | 4 |
| 10 | semantic search | 4 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 247 | 9.7 | [Large language models in medical and healthcare fields: applications, advances, and challenges](https://doi.org/10.1007/s10462-024-10921-0) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2024-09-20 | Dandan Wang, Shiqing Zhang |
| 2 | 166 | 5.2 | [TrafficGPT: Viewing, processing and interacting with traffic foundation models](https://doi.org/10.1016/j.tranpol.2024.03.006) | [Transport Policy](https://scholar4ai.com/journals/transport-policy.md) | 2024-03-14 | Siyao Zhang, Daocheng Fu, Wenzhe Liang et al. |
| 3 | 142 | 5.3 | [Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models](https://doi.org/10.1007/s10462-024-10896-y) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2024-08-10 | Zichao Lin, Shuyan Guan, Wending Zhang et al. |
| 4 | 104 | 5.9 | [Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence](https://doi.org/10.1109/tai.2025.3569516) | [IEEE Transactions on Artificial Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-artificial-intelligence.md) | 2025-05-13 | Timothy R. McIntosh, Teo Sušnjak, Nalin Asanka Gamagedara Arachchilage et al. |
| 5 | 55 | 2.6 | [Enhancing knowledge retrieval with in-context learning and semantic search through generative AI](https://doi.org/10.1016/j.knosys.2025.113047) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 2025-01-23 | Mohammed-Khalil Ghali, Abdelrahman Farrag, Daehan Won et al. |

## Used together with

Papers use Retrieval and QA (MS MARCO, Natural Questions, HotpotQA) most often with Wikipedia (30), Hugging Face Hub (17) and Twitter / X (9); it has 20 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

*What papers use Retrieval and QA (MS MARCO, Natural Questions, HotpotQA) together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 30 | 17.7 |
| [Hugging Face Hub](https://scholar4ai.com/datasets/hugging-face-hub.md) | Dataset | 17 | 21.8 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | Dataset | 9 | 1.2 |
| [CoNLL](https://scholar4ai.com/datasets/conll.md) | Dataset | 8 | 37.7 |
| [GSM8K / MATH](https://scholar4ai.com/datasets/gsm8k-math.md) | Dataset | 8 | 90.3 |
| [MMLU](https://scholar4ai.com/datasets/mmlu.md) | Dataset | 8 | 60.6 |
| [Reddit](https://scholar4ai.com/datasets/reddit.md) | Dataset | 8 | 7.9 |
| [Facebook](https://scholar4ai.com/datasets/facebook.md) | Dataset | 6 | 1.2 |
| [Natural language inference (SNLI, MultiNLI)](https://scholar4ai.com/datasets/natural-language-inference-snli-multinli.md) | Dataset | 6 | 38.2 |
| [BIG-bench](https://scholar4ai.com/datasets/big-bench.md) | Dataset | 5 | 96.5 |
| [SQuAD](https://scholar4ai.com/datasets/squad.md) | Dataset | 5 | 115.0 |
| [HumanEval / MBPP](https://scholar4ai.com/datasets/humaneval-mbpp.md) | Dataset | 4 | 70.3 |

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

## Cite this page

Scholar4AI. (2026). Retrieval and QA (MS MARCO, Natural Questions, HotpotQA). Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/retrieval-and-qa-ms-marco-natural-questions-hotpotqa

```bibtex
@misc{scholar4ai-datasets-retrieval-and-qa-ms-marco-natural-questions-hotpotqa,
  author = {{Scholar4AI}},
  title = {Retrieval and QA (MS MARCO, Natural Questions, HotpotQA)},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/retrieval-and-qa-ms-marco-natural-questions-hotpotqa}},
  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/).