Draft:Gretel AI

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Submission declined on 15 December 2024 by WeirdNAnnoyed (talk). This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are: in-depth (not just brief mentions about the subject or routine announcements) reliable secondary strictly independent of the subject Make sure you add references that meet all four of these criteria before resubmitting. Learn about mistakes to avoid when addressing this issue. If no additional references exist, the subject is not suitable for Wikipedia. If you would like to continue working on the submission, click on the "Edit" tab at the top of the window. If you have not resolved the issues listed above, your draft will be declined again and potentially deleted. If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors. Please do not remove reviewer comments or this notice until the submission is accepted. Where to get help If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews. If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed. How to improve a draft Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia. Help:Wikitext – how to use the markup Help:Referencing for beginners – how to include references Wikipedia:Article development – how to develop your article Wikipedia:Writing better articles – how to improve your article Wikipedia:Verifiability – make sure your article includes reliable third-party sources You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Add tags to your draft Editor resources Easy tools: Citation bot (help) | Advanced: Fix bare URLs Declined by WeirdNAnnoyed 57 days ago. Last edited 0 seconds ago. Reviewer: Inform author. This draft has been resubmitted and is currently awaiting re-review.

• Comment: The sources cited in this article are either connected to the subject (Gretel's own website), trivial passing mentions, or about other topics entirely. We need significant coverage about the company in secondary, reliable sources to have an article. If such sources can be found the article can be re-created. WeirdNAnnoyed (talk) 13:54, 15 December 2024 (UTC)

• Comment: I looked through the referenced papers and there's more than one reference for sure, two of them are explicitly about Gretel, one uses it as a comparison, and I added a fourth. If you think any are passing references, then let me know and I'll remove them. I also removed the link to Gretel's own website as a reference. Mckornfield

Gretel (also known as Gretel Labs or Gretel AI) is a software startup focused around creating high quality and private Synthetic data. Its primary focus is on generating textual, JSON or tabular data. It accomplishes this using a mix of privacy preservation tools (transformations, differential privacy) in concert with data generation tools (Large language models, and custom Fine-tuning (deep learning)).

Gretel's quality enforcement is accomplished by performing quality checks during data generation, thereby reducing the amount of low quality data in the final dataset.

This type of enforcement can also apply to privacy concerns, by using privacy filters or introducing appropriate levels of noise during data generation.