This is an important development for editors to be aware of - we're going
to have to be increasingly on the lookout for sources using ML-generated
bullshit. Here are two instances I'm aware of this week:
https://www.thenation.com/article/culture/internet-archive-publishers-lawsu…
In late February, Tyler Cowen, a libertarian economics
professor at George
Mason University, published a blog post titled
<https://web.archive.org/web/20230305055906/https:/marginalrevolution.com/marginalrevolution/2023/02/who-was-the-most-important-critic-of-the-printing-press-in-the-17th-century.html>,
“Who was the most important critic of the printing press in the 17th
century?” Cowen’s post contended that the polymath and statesman Francis
Bacon was an “important” critic of the printing press; unfortunately, the
post contains long, fake quotes attributed to Bacon’s *The Advancement of
Learning *(1605), complete with false chapter and section numbers.
Tech writer Mathew Ingram drew attention to the fabrications a few days
later
<https://newsletter.mathewingram.com/tyler-cowen-francis-bacon-and-the-chatgpt-engine/>,
noting that Cowen has been writing approvingly about the AI chatbot
ChatGPT
<https://marginalrevolution.com/marginalrevolution/2023/02/how-should-you-talk-to-chatgpt-a-users-guide.html>
for
some time now; several commenters on Cowen’s post assumed the fake quotes
must be the handiwork of ChatGPT. (Cowen did not reply to e-mailed
questions regarding the post by press time, and later removed the post
entirely, with no explanation whatsoever. However, a copy remains at the
Internet Archive’s Wayback Machine).
https://www.vice.com/en/article/3akz8y/ai-injected-misinformation-into-arti…
An article claiming to identify misinformation in an Oscar-winning
documentary about imprisoned Russian dissident Alexei Navalny is itself
full of misinformation, thanks to the author using AI.
Investigative news outlet *The Grayzone* recently published an article
<https://thegrayzone.com/2023/03/13/oscar-navalny-documentary-misinformation/>
that included AI-generated text as a source for its information. The
piece
<http://web.archive.org/web/20230314131551/https://thegrayzone.com/2023/03/13/oscar-navalny-documentary-misinformation/>,
“Oscar-winning ‘Navalny’ documentary is packed with misinformation” by Lucy
Komisar, included hyperlinks to PDFs
<http://web.archive.org/web/20230314121144/https://www.thekomisarscoop.com/wp-content/uploads/2023/02/Many-contributors-have-backgrounds-that-suggest-they-are-biased-in-favor-of-western-governments-and-against-its-enemies.pdf>
uploaded to the author’s personal website that appear to be screenshots
of conversations she had with ChatSonic, a free generative AI chatbot that
advertises itself as a ChatGPT alternative that can “write factual trending
content” using Google search results.
That said, I don't think this is anything to be too stressed about; the
Grayzone is already a deprecated source and blogs like Marginal Revolution
are treated with caution, though Cowen has sufficient credentials to be
treated as a reliable expert.
On Fri, Mar 17, 2023 at 11:23 AM Kimmo Virtanen <kimmo.virtanen(a)wikimedia.fi>
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. 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.
- Possible models
- 20b Neo(X) <https://github.com/EleutherAI/gpt-neox> by EleutherAI
(Apache 2.0)
- Fairseq Dense <https://huggingface.co/KoboldAI/fairseq-dense-13B> by
Facebook (MIT-licence)
- LLaMa
<https://ai.facebook.com/blog/large-language-model-llama-meta-ai/> by
Facebook (custom license, leaked research use only)
- Bloom <https://huggingface.co/bigscience/bloom> by Bigscience (custom
license <https://huggingface.co/spaces/bigscience/license>. open,
non-commercial)
2.) Fine-tuning or align
- Example: Standford Alpaca is ChatGPT fine-tuned LLaMa
- Alpaca: A Strong, Replicable Instruction-Following Model
<https://crfm.stanford.edu/2023/03/13/alpaca.html>
- Train and run Stanford Alpaca on your own machine
<https://replicate.com/blog/replicate-alpaca>
- Github: Alpaca-LoRA: Low-Rank LLaMA Instruct-Tuning
<https://github.com/tloen/alpaca-lora>
3.) 8,4,3 bit-quantization of model for reduced hardware requirements
- Running LLaMA 7B and 13B on a 64GB M2 MacBook Pro with llama.cpp
<https://til.simonwillison.net/llms/llama-7b-m2>
- Github: bloomz.cpp <https://github.com/NouamaneTazi/bloomz.cpp> &
llama.cpp <https://github.com/ggerganov/llama.cpp> (C++ only versions)
- Int-4 LLaMa is not enough - Int-3 and beyond
<https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-int-3-and>
- How is LLaMa.cpp possible?
<https://finbarrtimbers.substack.com/p/how-is-llamacpp-possible>
4.) Easy-to-use interfaces
- Transformer.js <https://xenova.github.io/transformers.js/> (WebAssembly
libraries to run LLM models in the browser)
- Dalai <https://github.com/cocktailpeanut/dalai> ( run LLaMA and
Alpaca in own computer as Node.js web service)
- web-stable-diffusion <https://github.com/mlc-ai/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(a)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.
- Possible models
- 20b Neo(X) <https://github.com/EleutherAI/gpt-neox> by
EleutherAI (Apache 2.0)
- Fairseq Dense <https://huggingface.co/KoboldAI/fairseq-dense-13B> by
Facebook (MIT-licence)
- LLaMa
<https://ai.facebook.com/blog/large-language-model-llama-meta-ai/> by
Facebook (custom license, leaked research use only)
- Bloom <https://huggingface.co/bigscience/bloom> by Bigscience (custom
license <https://huggingface.co/spaces/bigscience/license>. open,
non-commercial)
2.) Fine-tuning or align
- Example: Standford Alpaca is ChatGPT fine-tuned LLaMa
- Alpaca: A Strong, Replicable Instruction-Following Model
<https://crfm.stanford.edu/2023/03/13/alpaca.html>
- Train and run Stanford Alpaca on your own machine
<https://replicate.com/blog/replicate-alpaca>
- Github: Alpaca-LoRA: Low-Rank LLaMA Instruct-Tuning
<https://github.com/tloen/alpaca-lora>
3.) 8,4,3 bit-quantization of model for reduced hardware requirements
- Running LLaMA 7B and 13B on a 64GB M2 MacBook Pro with llama.cpp
<https://til.simonwillison.net/llms/llama-7b-m2>
- Github: bloomz.cpp <https://github.com/NouamaneTazi/bloomz.cpp> &
llama.cpp <https://github.com/ggerganov/llama.cpp> (C++ only versions)
- Int-4 LLaMa is not enough - Int-3 and beyond
<https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-int-3-and>
- How is LLaMa.cpp possible?
<https://finbarrtimbers.substack.com/p/how-is-llamacpp-possible>
4.) Easy-to-use interfaces
- Transformer.js <https://xenova.github.io/transformers.js/> (WebAssembly
libraries to run LLM models in the browser)
- Dalai <https://github.com/cocktailpeanut/dalai> ( run LLaMA and
Alpaca in own computer as Node.js web service)
- web-stable-diffusion
<https://github.com/mlc-ai/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(a)gmail.com>
wrote:
On Sun, Mar 5, 2023 at 8:39 PM Luis (lu.is) <luis(a)lu.is> wrote:
On Feb 22, 2023 at 9:28 AM -0800, Sage Ross <
ragesoss+wikipedia(a)gmail.com>gt;, 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.
https://commoncrawl.github.io/cc-crawl-statistics/plots/domains
<https://twitter.com/GuillaumeLample/status/1629151234597740550>
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