What Is Chatgpt And Why Does It Matter Heres Everything You Need To Know – ChatGPT can automatically produce something that reads even as deeply as human handwriting is amazing, unexpected. But how does she do it? And why does it work? My purpose here is to give a solid explanation of what goes on inside ChatGPT—and then explore why that’s good for producing what we might consider meaningful text. I should say at the outset that I’m going to focus on the big picture of what’s going on – and while I’m going to mention some engineering details, I’m not going to go too deep into them. (And the essence of what I’m going to say applies just as much to existing “major language models” [LLMs] as ChatGPT.)
The first thing to explain is that what ChatGPT always tries to do is to generate a “reasonable continuation” of every text found so far, where by “reasonable” we mean “what might be one would expect someone to write after seeing what people have written on billions of web pages, etc.
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“. Imagine scanning through billions of pages of human-written text (say, websites and digitized books) and finding all the examples of this text—and then seeing what the next word is in a fraction of the time. ChatGPT effectively does something like this, except (as I’ll explain) it doesn’t look at the actual text; it looks for things that are particularly “meaningful”. But the end result is to produce a list of words that may follow, along with “probability”:
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The amazing thing is that when ChatGPT does something like writing an essay what it’s actually doing is asking itself over and over again “given the text so far, what’s the next word?” -and every time a word is added. (Precisely, as I will explain, it adds a “symbol”, which can be part of the word, which is why it can sometimes create “new words”.)
But, OK, each step gets a list of words with probability. But who actually chooses to include the text (or whatever) they are writing? One might think that it is the “highest ranking” word (ie, the one assigned the highest “probability”). But this is where a bit of voodoo starts to come into play. Because for some reason – maybe one day we will have an understanding in a scientific way – if we always pick the word with the highest rank, we will usually find a very good one. a “flat” composition, which never seems to “show creativity” (and even sometimes repeats it verbatim). But if we sometimes (randomly) pick words of lower rank, we get an “interesting” essay.
The fact that there is a coincidence here means that if we use the same thing several times, we will probably get different texts each time. And, in keeping with the voodoo concept, there is a special one called “temperature” which determines how often low-ranking words will be used, and in the text generation, it turns out that the “temperature” of 0.8 looks the best. (It is worth emphasizing that there is no “theory” used here; it is only a matter of what has been found to work in practice. And for example the concept of “temperature” exists because of the well-known exponential distribution in physics. statistics happen to be used. , but there is no “physical” connection—at least as far as we know.)
Before I continue I must explain that for demonstration purposes we will not often use the full system in ChatGPT; Instead we will usually work with the simple GPT-2 system, which has a good enough image to run on a normal computer. And so everything that I show you I’ll be able to add in plain Wolfram Language code that you can run right away on your computer. (Click here on each image to copy the code behind it.)
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For example, here’s how to get the probability table above. First, we must generate the “language pattern” below:
Later, we will look inside this neural network, and talk about how it works. But now we can only apply the “pure model” as a black box to our text so far, and ask for the top 5 words with the highest probability that the model says should be followed:
Here’s what happens if one repeatedly uses the “model”—at each step adding the word with the highest probability (defined in this code as the “decision” of the model):
What if one goes further? In this (“zero temperature”) case it quickly becomes confusing and repetitive:
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But what if instead of always choosing the word “high” one sometimes randomly chooses the words “non-high” (with “randomness” equal to “temperature” 0.8)? Again one can construct the text:
Each time one does this, random choices will be made and the text will be different—like these 5 examples:
It is worth pointing out that even at the first step there are many possible “next words” to choose from (temperature 0.8), although their possibilities fall quickly (and, yes, the direct line of This log-log page corresponds to a
So what happens if one continues for a long time? Here’s a random example. It’s better than the word above (zero temperature), but it’s still weird:
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This is done in the simplest form of GPT-2 (as of 2019). On newer and older GPT-3 models the results are better. Here is the above word (zero temperature) text produced in the same “quick”, but with the main GPT-3 format:
OK, so ChatGPT always chooses its next word based on probability. But where does that probability come from? Let’s start with a simpler problem. Let’s think about creating English text one letter (per word) at a time. How do we know what the probability of each letter is?
The least we can do is just take a sample of English text, and count the number of times different letters occur. So, for example, this counts the letters in the Wikipedia article on “cats”:
The result is similar, but not the same (“o” is no doubt common in the article “dog” because, after all, it occurs in the word “dog” itself). Still, if we take a large enough sample of English text we can expect to eventually get fairly consistent results:
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Here’s a sample of what we get if we just generate a series of letters with this probability:
We can break this down into “words” by adding spaces as if they were letters with a certain probability:
We can do a slightly better job of rendering “words” by forcing the distribution of “word length” to follow what is written in English:
We don’t happen to get “real words” here, but the results look a little better. To go further, though, we need to do more than just pick each letter individually at random. And, for example, we know that if we have “q”, the next letter should basically be “u”.
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And here is a picture showing the possibilities of two letters (“2-grams”) in standard English text. The first possible letters appear across the page, and the second letters move down the page:
And we see here, for example, that the column “q” is empty (probability zero) except for the row “u”. OK, so now instead of generating our “words” one letter at a time, let’s generate them looking at two letters at a time, using the possibility of “2-gram”. Here’s a sample of the results—which include a few “real words”:
With a good enough English text we can get very good estimates not only of the possibility of single letters or pairs of letters (2-grams), but also of long letters. And if we generate “random words” that get progressively longer
But let’s assume now—more or less like ChatGPT does—that we’re dealing with whole words, not letters. There are about 40,000 words that are reasonably used in English. And by looking at a large body of English text (say several million books, with a total of several hundred billion words), we can get an estimate of how common each word is. Using this we can start creating “sentences”, in which each word is independently selected at random, with the same probability of appearing in the corpus. Here’s a sample of what we get:
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No wonder, this is nonsense. So how can we do better? As with letters, we can begin to take into account not only the probabilities of single words but the probabilities of pairs or longer.
Gram of words. Doing this in pairs, here are 5 examples of what we find, all cases starting with the word “cat”:
It gets a little “realistic look”. And we may imagine if we are able to use it for a long time
Grammarly we would basically “find ChatGPT” – meaning we would find something that could generate a series of text-texts with “probability of the correct total text”. But here’s the problem: there isn’t even close to enough English text ever written to be able to rule out that possibility.
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A web crawl may contain several hundred billion words; books that
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