I swear, the big reason models are black boxes are because we _want_ them to be. There's clear anti-sentiment mentality against people doing theory and the result of this shows. I remember not too long ago Yi Tay (under @agihippo but main is @YiTayML) said "fuck theorists". I guess it's not a surprise Deep Mind recently hired him after that "get good" stuff.
Also, I'd like to point out, the author uses "we" but the paper only has one author on it. So may I suggest adding their cat as a coauthor? [0]
> I've been told I'm "gatekeeping"
I mean...when the alternative is politely (better yet - excitedly) answering the question asked? You kind of are.
> I swear, the big reason models are black boxes are because we _want_ them to be.
Talk is cheap.
> I guess it's not a surprise Deep Mind recently hired him after that "get good" stuff.
I agree "fuck theorists" is in no way constructive. But, Deep Mind has objectively helped move the field forward. And your criticism of "get good" stuff? Did you not just tell people to "learn math" rather than help them to understand it yourself? That's the _exact_ meaning of the phrase "get good" on the internet. At best you're both being about as toxic (at least from your own description).
> when the alternative is politely (better yet - excitedly) answering the question asked? You kind of are.
But yes, hurdles exist and they are not bad. I sure as hell don't want someone that can't do calculus designing rocket engines. And you probably don't want a rocket engineer performing surgery on you. Call them what you will, but it's not a bouncer at the door telling you you're not "pretty enough", which is what gatekeeping is generally used to refer to.
> Talk is cheap.
But I have no idea how to respond to this comment. What I said was fairly broad and this response is broader. Are you asking for "proof"? Of what? Interpretability? Is not the article proof of it to some degree? Or Golden Gate Claude?
> Did you not just tell people to "learn math" rather than help them to understand it yourself?
> you're both being about as toxic
So I will add more context to clarify my complaints, for any of those interested. I specifically called out Mission Impossible Language Models[0], so what's that about? I suggest reading the paper. The authors create a continuum of difficulties in impossible languages. The hardest being a random word ordering. The claim is that LLMs can't learn impossible languages just as well as natural languages. It's fairly easy to understand the error in this work. They use perplexity, which is sometimes called "surprisal." You take it conditioned on the previous words and you calculate what is likely to come next. But perplexity doesn't tell you that the model didn't learn the language, or even efficiently. The metric isn't going to work for a one-to-one comparison with a structured language. The reason being that there is naturally more entropy in the impossible language. Frankly, because there are more words that are equally likely to come next. It's comparing coin flips to dice throws.
Let's use an example: our target sentence will be "My name is godelski." In a random shuffle language we have 4! (24) ways to represent that sentence that are all __equivalent__. That's the key part. In natural language, all I can think of is 2 ("Godelski, my name is" as a highly unlikely alternative). So in natural language if we have "My name" and are predicting the next word, "is" is pretty likely. But in the random language "is" is just as likely as "<name>". This isn't proof that the language isn't learned, it is just that the language isn't structured. "My name is godelski" and "My name godelski is" are equivalent sentences in a random ordering language. But actually, this gets even harder because the tokenization was trained on natural word order. If you look at Table 1 you'll see how this gets messy (notice that "bookshelf" is the tokens " books" (space intentional) "he" "lf"). The picture gets clearer when you look at how they prepared the data (it isn't shuffled each time the model gets the data, it is shuffled once and then the model is trained on that. This is not the same as the random language and unless you're really lucky, there's going to be certain patterns more common than others and so that'll just make it more difficult for the model. The dataloader should shuffle sentences, which will teach the model to ignore the patterns. You should also measure perplexity against all valid predictions, not a single one. This one is a killer for me).
Side note:
> fear of being judged:
Clearly, you have judged me quite harshly.