Hi all

I agree that the AI creators should attribute Wikipedia as their source. But on the other hand, when the result is incorrect etc we might actually be glad that they do not attribute it.
The issue is how to convince readers to come to the source (our projects) rather than using in-between steps (AI or otherwise).

Matej

On Sat, Mar 18, 2023 at 3:22 AM Steven Walling <steven.walling@gmail.com> wrote:
On Fri, Mar 17, 2023 at 6:03 PM The Cunctator <cunctator@gmail.com> wrote:
I really feel like we're getting into pretty aggressive corporate abuse of the Wikipedia copyleft.

I completely agree. It makes me pretty angry that Wikipedians have spent millions of volunteer hours creating content to educate and inform people as accurately as we can, and it's being used to generate convincing but often wildly misleading bullshit. 

The ground truth on what generated AI content is (from a copyright position) and where authorship/ownership lies seems to be rapidly evolving. The U.S. Copyright Office recently refused to issue copyrights for some AI-generated works, seemingly on the principle that they lack human authorship / are essential to contracting work for hire from an artist or writer. 

IANAL of course, but to me this implies that responsibility for the *egregious* lack of attribution in models that rely substantially on Wikipedia is violating the Attribution requirements of CC licenses. Just like the Foundation took a principled position in testing the legality of warrantless mass surveillance, I would love to see us push back on the notion that it's legal or moral for OpenAI or any of these other companies to take our content and use it to flood the Internet with machine-generated word diarrhea. 
 
On Fri, Mar 17, 2023, 4:45 PM Adam Sobieski <adamsobieski@hotmail.com> wrote:
Hello,

I would like to indicate "Copilot" in the Edge browser as being potentially relevant to Wikipedia [1][2].

It is foreseeable that end-users will be able to open sidebars in their Web browsers and subsequently chat with large language models about the contents of specific Web documents, e.g., encyclopedia articles. Using Web browsers, there can be task contexts available, including the documents or articles in users' current tabs, potentially including users' scroll positions, potentially including users' selections or highlightings of content.

I, for one, am thinking about how Web standards, e.g., Web schema, can be of use for amplifying these features and capabilities for end-users.


Best regards,
Adam Sobieski

[1] https://learn.microsoft.com/en-us/deployedge/microsoft-edge-relnote-stable-channel?ranMID=24542#version-1110166141-march-13-2023
[2] https://www.engadget.com/microsoft-edge-ai-copilot-184033427.html

From: Kimmo Virtanen <kimmo.virtanen@wikimedia.fi>
Sent: Friday, March 17, 2023 8:17 AM
To: Wikimedia Mailing List <wikimedia-l@lists.wikimedia.org>
Subject: [Wikimedia-l] Re: Bing-ChatGPT
 
Hi,

The development of open-source large language models is going forward. The GPT-4 was released and it seems that it passed the Bar exam and tried to hire humans to solve catchpas which were too complex. However, the development in the open source and hacking side has been pretty fast and it seems that there are all the pieces for running LLM models in personal hardware (and in web browsers). Biggest missing piece is fine tuning of open source models such as Neox for the English language. For multilingual and multimodal (for example images+text) the model is also needed.


So this is kind of a link dump for relevant things for creation of open source LLM model and service and also recap where the hacker community is now.


1.) Creation of an initial unaligned model. 


2.) Fine-tuning or align


3.) 8,4,3 bit-quantization of model for reduced hardware requirements


4.) Easy-to-use interfaces

  • Transformer.js (WebAssembly libraries to run LLM models in the browser)
  • Dalai  ( run LLaMA and Alpaca in own computer as Node.js web service)
  • web-stable-diffusion (stable diffusion image generation in browser)

Br,
-- Kimmo Virtanen

On Fri, Mar 17, 2023 at 1:53 PM Kimmo Virtanen <kimmo.virtanen@gmail.com> wrote:
Hi,

The development of open-source large language models is going forward. The GPT-4 was released and it seems that it passed the Bar exam and tried to hire humans to solve catchpas which were too complex to it. However, the development in open source and hacking side has been pretty fast and it seems that there is all the pieces for running LLM models in personal hardware (and in web browser). Biggest missing piece is fine tuning of open source model such as Neox for english language. For multilingual and multimodal (for example images+text) the model is also needed.


So this is kind of link dump for relevant things for creation of open source LLM model and service and also recap where hacker community is now.


1.) Creation of an initial unaligned model. 


2.) Fine-tuning or align


3.) 8,4,3 bit-quantization of model for reduced hardware requirements


4.) Easy-to-use interfaces

  • Transformer.js (WebAssembly libraries to run LLM models in the browser)
  • Dalai  ( run LLaMA and Alpaca in own computer as Node.js web service)
  • web-stable-diffusion (stable diffusion image generation in browser)

Br,
-- Kimmo Virtanen

On Mon, Mar 6, 2023 at 6:50 AM Steven Walling <steven.walling@gmail.com> wrote:


On Sun, Mar 5, 2023 at 8:39 PM Luis (lu.is) <luis@lu.is> wrote:
On Feb 22, 2023 at 9:28 AM -0800, Sage Ross <ragesoss+wikipedia@gmail.com>, wrote:
Luis,

OpenAI researchers have released some info about data sources that
trained GPT-3 (and hence ChatGPT): https://arxiv.org/abs/2005.14165

See section 2.2, starting on page 8 of the PDF.

The full text of English Wikipedia is one of five sources, the others
being CommonCrawl, a smaller subset of scraped websites based on
upvoted reddit links, and two unrevealed datasets of scanned books.
(I've read speculation that one of these datasets is basically the
Library Genesis archive.) Wikipedia is much smaller than the other
datasets, although they did weight it somewhat more heavily than any
other dataset. With the extra weighting, they say Wikipedia accounts
for 3% of the total training.

Thanks, Sage. Facebook’s recently-released LLaMa also shares some of their training sources, it turns out, with similar weighting for Wikipedia - only 4.5% of training text, but more heavily weighted than most other sources:

https://twitter.com/GuillaumeLample/status/1629151234597740550

Those stats are undercounting, since the top source (CommonCrawl) also itself includes Wikipedia as its third largest source. 

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