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

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

← Code and data repositories cited in research papers

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.

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#

20232026

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	1
Neural Computing and Applications	1
Nature Machine Intelligence	1

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	Nature Machine Intelligence	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	Neural Computing and Applications	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	Expert Systems with Applications	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

@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}
}

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