unitaryai/detoxify: papers linking to it

GitHub · 1,312 stars · 149 forks · Python · license Apache-2.0 · created 2020 · github.com/unitaryai/detoxify

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to unitaryai/detoxify (https://github.com/unitaryai/detoxify), 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#

5 papers in the tracked journals link to the GitHub repository unitaryai/detoxify in their Crossref records, the first in 2023; most of them (2) in 2026.
By category: AI (5); the journals with the most are Expert Systems with Applications (1), Applied Intelligence (1) and International Journal of Computer Vision (1).

By year#

20232026

Papers linking to unitaryai/detoxify by publication year
Year	Papers
2023	1
2024	1
2025	1
2026	2

Journals#

Journals whose papers link to unitaryai/detoxify most
Journal	Papers
Expert Systems with Applications	1
Applied Intelligence	1
International Journal of Computer Vision	1
Neural Networks	1
Cognitive Computation	1

Papers linking to unitaryai/detoxify, most cited first
Published	Title	Journal	Authors	Citations	Link
2023-09-28	CoDeS: A Deep Learning Framework for Identifying COVID-Caused Depression Symptoms	Cognitive Computation	Mudasir Ahmad Wani, Mohammed A. ELAffendi, Patrick Bours et al.	16	cites it
2024-04-01	MODE: a multimodal open-domain dialogue dataset with explanation	Applied Intelligence	Hang Yin, Pinren Lu, Ziang Li et al.	3	cites it
2026-03-06	T2VShield: Model-Agnostic Jailbreak Defense for Text-to-Video Models	International Journal of Computer Vision	Siyuan Liang, Jiayang Liu, Jiecheng Zhai et al.	2	cites it
2026-02-10	State-wise linear modulation (SLim): A novel approach for steering large language models	Neural Networks	Basavaraj hiremath, Marco Polignano, Marco Levantesi et al.	1	cites it
2025-12-01	WITHDRAWN: CCTAgent: Few-Shot Cloaked Chinese Toxic Text Detection with LLM Agents	Expert Systems with Applications	Bingyang Guo, Mingxuan Liu, Cheng Sheng Tu et al.	0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). unitaryai/detoxify: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-unitaryai-detoxify

@misc{scholar4ai-code-github-unitaryai-detoxify,
  author = {{Scholar4AI}},
  title = {unitaryai/detoxify: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-unitaryai-detoxify}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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