---
title: "GPT-Neo: Large Scale Autoregressive Language Modeling…"
description: "3 papers in the tracked journals link to the Zenodo dataset GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow in their Crossref records, the first in 2023; most of them (2) in 2023."
url: https://scholar4ai.com/code/zenodo-5297715
markdown_url: https://scholar4ai.com/code/zenodo-5297715.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "3 papers in the tracked journals link to the Zenodo dataset GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow in their Crossref records, the first in 2023; most of them (2) in 2023. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow: papers linking to it

Research papers in the 480 tracked journals that link to GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow (https://doi.org/10.5281/zenodo.5297715), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- Zenodo
- Software
- 2021
- archives github.com/eleutherai/gpt-neo
- https://doi.org/10.5281/zenodo.5297715

All repositories: https://scholar4ai.com/code.md

## Key findings

- 3 papers in the tracked journals link to the Zenodo dataset GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow in their Crossref records, the first in 2023; most of them (2) in 2023.
- By category: AI (3); the journals with the most are Expert Systems with Applications (1), Neural Computing and Applications (1) and Nature Machine Intelligence (1).

## By year

*Papers linking to GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow by publication year*

| Year | Papers |
|---|---:|
| 2023 | 2 |
| 2026 | 1 |

## Journals

*Journals whose papers link to GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow most*

| Journal | Papers |
|---|---:|
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 1 |
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 1 |

## Papers

*Papers linking to GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-10-23 | [Neural scaling of deep chemical models](https://doi.org/10.1038/s42256-023-00740-3) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Nathan C. Frey, Ryan Soklaski, Simon Axelrod et al. | 125 | cites it |
| 2023-03-09 | [DeepPress: guided press release topic-aware text generation using ensemble transformers](https://doi.org/10.1007/s00521-023-08393-4) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Abir Rahali, Moulay A. Akhloufi | 1 | cites it |
| 2026-04-23 | [AutoWAFuzzer: An adaptive framework for web application firewall penetration testing with multi-agent system and RAG-enabled reinforcement learning](https://doi.org/10.1016/j.eswa.2026.132546) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Phan The Duy, Nguyen Ngoc Thanh, Pham Cong Lap et al. | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/zenodo-5297715

```bibtex
@misc{scholar4ai-code-zenodo-5297715,
  author = {{Scholar4AI}},
  title = {GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/zenodo-5297715}},
  urldate = {2026-11-03},
  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/).