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Gpt-4 has been released!. Looks like the cat is finally out of the bag. The CW implications of large language models are obvious and have been discussed here, so I figured I would drop a few fun facts.
Also, here's a peek at LessWrong freaking out.
The full technical report gives some fascinating information. Here are some highlights:
GPT-4 can pass a bar exam and score a 5 on several AP exams.
GPT-4 is 82% less likely to respond to requests for disallowed content and 40% more likely to produce factual responses than GPT-3.5 on our internal evaluations.
GPT-4 can accept images as inputs and generate captions, classifications, and analyses.
GPT-4 is capable of handling over 25,000 words of text, allowing for use cases like long form content creation, extended conversations, and document search and analysis.
Of all of these, passing the bar exam is the one that sticks out. We'll have to see how much it still hallucinates, but this is clearly a water mark, at least for the legal profession.
I'll go ahead and stake a perhaps dramatic but I believe warranted claim - the culture war is about to get ugly. Creating ads, propaganda, and bots to argue politics has never been easier. Whichever side moves first on scaling and implementing these language models to persuade humans to their camp will own the future.
Using the model here using it's GRE scores.
GPT-3.5 Verbal IQ = 118
GPT-3.5 Quantitative IQ = 101
GPT-4 Verbal IQ = 144
GPT-4 Quantitative IQ = 130
There's an updated version of that here on which the score is even higher. Although there is probably plenty of online info on standardized tests and whatnot in its training set, if it's from scrubbing the internet, so I doubt you can infer a ton about how "smart" it is in general from these. The revised conversion yields Verbal IQ=146, Quant IQ=135.5, overall IQ=145.4.
This has been an annoying aspect of LLM AI hype. There are plenty of indicators of something going on but many of the test results are not of that set. If you train them on the question sets and answer keys for repeatably mechanically gradable exams like the SAT, GRE or bar exams then it should be expected that they will perform well on them.
What would be really nice is if whenever the AI produced content it had to also tell you the minimal edit distance between that content and some content (or a direct combination of contents) in its training set. That way you could have a good measure of how much original content it was actually producing vs. how much it was just paraphrasing its training set. Or at least it would be useful to have extensive data on the average edit distance between a response and some item in the corpus.
Your proposed method doesn't work - even if you just turned a query into an embedding, picked the closest text in its dataset, and then ran that text through google translate and back a few times to obfuscate word choice, order, and other things like that, it'd change enough that literal edit distance would still be very high.
An analogy to image models, here's a claimed example of of taking inspiration from a particular photo in a training set. It's really not that close.
Not really, no. Edit distance is relative to which operations count as primitive "edits" and the "cost" of each use of that operation. There are specific forms of edit distance of which what you are saying is true, but you could also have an edit distance where "run it through Google Translate" is a primitive edit operation. Obviously, you would have to pick the operations to fit the specific model, e.g. what external resources it has access to.
Okay, I assumed you meant character-level edit distance, because that's what the article you linked was exclusively about. But without that, 'edit distance' isn't really a useful term, as we don't know what a 'primitive operation' is in the context of a LLM because we do not know that much if anything about what they actually do internally.
Based on what I've seen, 'most similar example in training set' doesn't capture the extent to which LLMs memorize things. Even if they are memorizing a lot, it's memorization in a very complicated way - otherwise the 'write a story about X in the style of Y', where X and Y hadn't ever been done before, just wouldn't work.
how is this memorizing? or this? Like, there's some extent to which it's memorizing things more than humans do, certainly. But it's a very vague sense, and positing a metric like edit distance that fully captures that sense just restates the problem, because we don't know what that is
That isn’t true, the formal definition doesn’t restrict what operations there are (if you disagree then you should quote it and tell me where), it just requires that they be operations on strings. Whatever else using Google Translate is, it’s an operation on strings.
I’m not suggesting that we base primitive operations on what the LLM does internally. Your initial example wasn’t about what the LLM does internally, it was about “what if the LLM ran things through Google Translate a few times to trick you.” I’m saying you could supplement a more basic measure with additional operations to capture when the model has access to external programs like Google Translate.
I never said that the AI was just “memorizing” things, that’s an obvious strawman. All I said was that edit distance would help you tell how much the AI was paraphrasing from its training set, which doesn’t presuppose that it’s not paraphrasing very little. You seem to have misinterpreted me as saying that an edit distance would show the AI was paraphrasing a lot, when all I said was an edit distance would be a helpful metric of how much it was. I didn’t make any specific commitments about how much that would be across a wide range of cases. Nor did I suggest that an edit distance would fully capture things, just that it would give a good sense of how close things were.
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