LOL! I think these results speak more to the flexibility of Perl than any actual insight on the training data! After all, 93% of inkblots are valid Perl scripts: https://www.mcmillen.dev/sigbovik/
The prominence of AppleScript ought to have been a pretty big red flag: the author seems to be claiming the model was trained on more AppleScript than Python, which simply can’t be true.
Ironically LLMs seem pretty bad at writing AppleScript, I think because (i) the syntax is English-like but very brittle, (ii) the application dictionaries are essential but generally not on the web, and (iii) most of the AppleScript that is on the web has been written by end users, often badly.
It seems to be an error with the classifier. Sorry everyone. I probably shouldn't have posted that graph; I knew it was buggy, I just thought that the Perl part might be interesting to people.
They are not full programs, just code translating to numbers and strings.
I used an LLM to generate an inkblot that translates to a Python string and number along with verification of it, which just proves that it is possible.
the way that the quoted article creates Perl programs is through OCRing the inkblots (i.e. creating almost random text) and then checking that result to see if said text is valid Perl
No, asking an LLM to generate the inkblot is the same as asking the LLM to write a string and then obfuscating it in an inkblot.
OCRing literal random inkblots will not produce valid C (or C# or python) code, but it will prodce valid Perl most of the time, because Perl is weird, and that is funny.
It's not about obfuscating text in inkblot, it's about almost any string being a valid Perl program, which is not the case for most languages
Yes. That was the whole point of the original comment you were misunderstanding.
Because of the flexibility of Perl and heavy amount of symbol usage, you can in fact run most random combinations of strings and they’ll be valid Perl.
Most random unquoted strings are certainly not valid Python programs. I don't know Perl well enough to say anything about that but I know what you're saying certainly isn't true with Python.
Jack has a lot of really bad takes and frequently makes lots of mistakes. Honestly, I don't know why people take him seriously.
I mean you can go read his blog post that's pinned where he argues that there's no new ideas and it is all data. He makes the argument that architecture doesn't matter, which is just so demonstrably false that it is laughable. He's a scale maximalist.
I also expect an AI researcher from a top university to not make such wild mistakes
> 3. RLHF: first proposed (to my knowledge) in the InstructGPT paper from OpenAI in 2022
I mean if you go read the instruct paper on page 2 you'll see
| Specifically, we use reinforcement learning from human feedback (RLHF; Christiano et al., 2017; Stiennon et al., 2020) to fine-tune GPT-3 to follow a broad class of written instructions (see Figure 2).
Where in Christiano you'll find
|| Our algorithm follows the same basic approach as Akrour et al. (2012) and Akrour et al. (2014)
I mean this is just obviously wrong. It is so obviously wrong it should make the person saying it second guess themselves (which is categorically the same error you're pointing out).
I'm sure we can trace the idea back to the 80's if not earlier. This is the kind of take I'd expect a non-researcher to have, but not someone with two dozen NLP papers. The Instruct-GPT paper was just the first time someone integrated RLHF into a LLM (but not a LM).
Maybe a better article is the one he wrote on Super Intelligence From First Principles. As usual, when someone says "First Principles" you bet they're not gonna start from First Principles... I guess this makes sense in CS since we index from 0
Hi again. I had already written about this later in my blog post (which is unrelated to this thread), but the point was that RLHF hadn't been applied to language models at scale until InstructGPT. I edited the post just now to clarify this. Thanks for the feedback!
The beginning of the text feels really odd, almost GPT-3-like at some points. At least, that's the way it seems when you do not know the jargon. It just rambles on and on. Thankfully it gets more coherent later on, but I am not sure what the author intended to say in this piece.
That "we're going to allow even bigger than 2% inflation" is the bullshit of bullshits because they've never even been able to hit that 2. Means all the printing's been useless and things are going to shit. (or have already)
A SPN will generate a unique random permutation, from which you can draw your random list of unique items. With a little of post-ptocessing, you can generate random permutations of arbitrary size.
As a result, OP seems to think the model was trained on a lot of Perl: https://xcancel.com/jxmnop/status/1953899440315527273#m
LOL! I think these results speak more to the flexibility of Perl than any actual insight on the training data! After all, 93% of inkblots are valid Perl scripts: https://www.mcmillen.dev/sigbovik/