---
title: "TruthfulQA / HellaSwag / ARC: papers using it"
description: "From Nov 2023 to Oct 2026, 69 archived papers mentioned TruthfulQA / HellaSwag / ARC in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Aug 2024 (6)."
url: https://scholar4ai.com/datasets/truthfulqa-hellaswag-arc
markdown_url: https://scholar4ai.com/datasets/truthfulqa-hellaswag-arc.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "From Nov 2023 to Oct 2026, 69 archived papers mentioned TruthfulQA / HellaSwag / ARC in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Aug 2024 (6). (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)
---

# TruthfulQA / HellaSwag / ARC

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

Benchmarks used to compare large language models on knowledge, reasoning, math and code.

- data category: LLM evaluation benchmarks
- 0 papers in the last 7 days
- Names searched for: TruthfulQA, HellaSwag, AI2 Reasoning Challenge, ARC-Challenge, ARC-Easy

Most-used data: https://scholar4ai.com/rankings/datasets.md · All LLM evaluation benchmarks datasets: https://scholar4ai.com/datasets/llm-evaluation-benchmarks.md



## Key findings

- From Nov 2023 to Oct 2026, 69 archived papers mentioned TruthfulQA / HellaSwag / ARC in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Aug 2024 (6).
- No paper of the tracked journals mentioned TruthfulQA / HellaSwag / ARC in the last 30 days (2026-10-07 to 2026-11-05).
- From 2017 to 2026 (so far), TruthfulQA / HellaSwag / ARC was mentioned in 70 papers in the tracked journals, most of them in AI (65), information systems (3) and finance (1).
- 26 of them were published in 2025, against 0 in 2017.
- Since 2023, the institutions with the most of these papers were Beihang University (4), University of Hong Kong (4) and Chinese University of Hong Kong (3).
- The journals publishing most of them were IEEE Transactions on Pattern Analysis and Machine Intelligence (9), Artificial Intelligence Review (6) and Expert Systems with Applications (6).
- 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 TruthfulQA / HellaSwag / ARC most often with MMLU (25), Hugging Face Hub (22) and Wikipedia (19); it has 29 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 | 25 | 33 | 69 |

## 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 | 0 | 4 | 12 | 24 | 25 |
| [Information Systems](https://scholar4ai.com/categories/information-systems.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 |
| [Finance](https://scholar4ai.com/categories/finance.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| [Management](https://scholar4ai.com/categories/management.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| All categories | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 12 | 26 | 28 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-pattern-analysis-and-machine-intelligence.md) | 9 |
| 2 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 6 |
| 3 | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 6 |
| 4 | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 4 |
| 5 | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 4 |
| 6 | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 4 |
| 7 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 4 |
| 8 | [AI & Society](https://scholar4ai.com/journals/ai-society.md) | 4 |
| 9 | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 4 |
| 10 | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 3 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Beihang University](https://scholar4ai.com/institutions/beihang-university.md) | 4 |
| 2 | [University of Hong Kong](https://scholar4ai.com/institutions/university-of-hong-kong.md) | 4 |
| 3 | [Chinese University of Hong Kong](https://scholar4ai.com/institutions/chinese-university-of-hong-kong.md) | 3 |
| 4 | [National University of Singapore](https://scholar4ai.com/institutions/national-university-of-singapore.md) | 3 |
| 5 | [Alibaba Group (China)](https://scholar4ai.com/companies/alibaba-group-china.md) | 2 |
| 6 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 2 |
| 7 | [East China Normal University](https://scholar4ai.com/institutions/east-china-normal-university.md) | 2 |
| 8 | [Harbin Institute of Technology](https://scholar4ai.com/institutions/harbin-institute-of-technology.md) | 2 |
| 9 | [Huazhong University of Science and Technology](https://scholar4ai.com/institutions/huazhong-university-of-science-and-technology.md) | 2 |
| 10 | KIIT University | 2 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 27 |
| 2 | United States | 15 |
| 3 | Hong Kong | 7 |
| 4 | Germany | 5 |
| 5 | Spain | 5 |
| 6 | Australia | 4 |
| 7 | United Kingdom | 4 |
| 8 | Singapore | 4 |
| 9 | India | 3 |
| 10 | Saudi Arabia | 3 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 45 |
| 2 | hallucination mitigation | 7 |
| 3 | [interpretability](https://scholar4ai.com/keywords/interpretability.md) | 5 |
| 4 | [generative AI](https://scholar4ai.com/keywords/generative-ai.md) | 4 |
| 5 | natural language processing | 4 |
| 6 | [ChatGPT](https://scholar4ai.com/keywords/chatgpt.md) | 3 |
| 7 | TruthfulQA | 3 |
| 8 | [generalization](https://scholar4ai.com/keywords/generalization.md) | 3 |
| 9 | hallucinations | 3 |
| 10 | llamas | 3 |

## 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 | 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. |
| 3 | 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. |
| 4 | 158 | 6.0 | [Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives](https://doi.org/10.1109/tai.2024.3444742) | [IEEE Transactions on Artificial Intelligence](https://scholar4ai.com/journals/ieee-transactions-on-artificial-intelligence.md) | 2024-08-19 | Desta Haileselassie Hagos, Rick Battle, Danda B. Rawat |
| 5 | 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. |

## Used together with

Papers use TruthfulQA / HellaSwag / ARC most often with MMLU (25), Hugging Face Hub (22) and Wikipedia (19); it has 29 partners in the code and data network, in the group around Twitter / X, Facebook, YouTube / TikTok.

*What papers use TruthfulQA / HellaSwag / ARC together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [MMLU](https://scholar4ai.com/datasets/mmlu.md) | Dataset | 25 | 216.3 |
| [Hugging Face Hub](https://scholar4ai.com/datasets/hugging-face-hub.md) | Dataset | 22 | 32.2 |
| [Wikipedia](https://scholar4ai.com/datasets/wikipedia.md) | Dataset | 19 | 12.8 |
| [GSM8K / MATH](https://scholar4ai.com/datasets/gsm8k-math.md) | Dataset | 16 | 206.3 |
| [ImageNet](https://scholar4ai.com/datasets/imagenet.md) | Dataset | 13 | 3.8 |
| [MT-Bench / Chatbot Arena](https://scholar4ai.com/datasets/mt-bench-chatbot-arena.md) | Dataset | 10 | 82.3 |
| [BIG-bench](https://scholar4ai.com/datasets/big-bench.md) | Dataset | 9 | 198.4 |
| [Pre-training corpora (Common Crawl, C4, The Pile)](https://scholar4ai.com/datasets/pre-training-corpora-common-crawl-c4-the-pile.md) | Dataset | 9 | 55.9 |
| [Twitter / X](https://scholar4ai.com/datasets/twitter-x.md) | Dataset | 9 | 1.4 |
| [HumanEval / MBPP](https://scholar4ai.com/datasets/humaneval-mbpp.md) | Dataset | 8 | 160.8 |
| [Natural language inference (SNLI, MultiNLI)](https://scholar4ai.com/datasets/natural-language-inference-snli-multinli.md) | Dataset | 8 | 58.2 |
| [CIFAR-10 / CIFAR-100](https://scholar4ai.com/datasets/cifar-10-cifar-100.md) | Dataset | 7 | 3.5 |

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

## Cite this page

Scholar4AI. (2026). TruthfulQA / HellaSwag / ARC. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/truthfulqa-hellaswag-arc

```bibtex
@misc{scholar4ai-datasets-truthfulqa-hellaswag-arc,
  author = {{Scholar4AI}},
  title = {TruthfulQA / HellaSwag / ARC},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/truthfulqa-hellaswag-arc}},
  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/).