81–90 of 213 posts
It's really not so complicated. This is just an issue with text tokenization, and the fact that the learning model never actually sees the raw input bytes. All modern LLMs use a tokenizer to convert a sequence of bytes into a sequence of tokens. Short, common words like "the" and "why" are represented as single tokens, while longer and less-common words are represented by multiple tokens. For example, the word "fanta…
There's a way simpler answer than this Type I Type II thinking stuff. Most LLMs like GPT are not trained on the level of individual characters – they process input and outputs on the level of subword units that compose multiple characters to support long context windows (i.e. "door" instead of "d", "o", "o", "r"). As a result, they do poorly on character manipulation tasks. You can get some insight here: https://beta…
This doesn't really explain it, because the following prompt works flawlessly: "Print each letter of the word "Artificial intelligence" on a new line. Each line should contain a single letter." But the following doesn't: "Print the ROT13 encoding of each letter of the word "Artificial intelligence" on a new line. Each line should contain a single letter." So it gets hung up specifically on ROT13.
Prompt: Print each letter of the word "Artificial intelligence" on a new line. Each line should contain a single letter.
ChatGPT: [prints "Artificial intelligence", each letter on its own line]
Prompt: Awesome! Now do ROT-13 for each letter.
ChatGPT: ROT-13 is a simple substitution cipher that replaces a letter with the letter 13 letters after it in the alphabet. For example, A becomes N and B becomes O. Since the alphabet has only 26 letters, when we reach the end of the alphabet we simply wrap around to the beginning.
To perform ROT-13 on each letter of "Artificial intelligence", we would do the following:
A becomes N r becomes e t becomes g i becomes v f becomes s i becomes v c becomes p i becomes v a becomes n l becomes y
i becomes v n becomes a t becomes g e becomes r l becomes y l becomes y i becomes v g becomes l e becomes r n becomes a c becomes p e becomes r
So the resulting string would be "Nvevpngvnyvgryvra".
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So letter by letter, is almost nailed it (except it encoded g as y instead of t), but then horribly failed joining back in (it says Airicatialitelien). I've tried it a couple more times and it seems crucial that it prints input letters (otherwise it becomes distracted and prints nonsense), but it absolutely can't join it back:
Prompt: Awesome! Now do ROT-13 for each letter. Print each letter of input, then ->, then the ROT-13 for that letter.
ChatGPT: [... produces mostly correct output]
Prompt: Now join that back in.
ChatGPT: Artificial intelligence -> Nvgvpnyyl ynathntrzrag
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(That's “Aitically languagement”)
UPD: Here's another one: https://u.ale.sh/Nrtevfvpv.png
A lot of people seem to be overlooking the fact that it's missing a huge piece of the puzzle, and that is being able to learn. This is a model frozen in time, you can explain to it a hundred times why it's wrong and it will learn nothing. Until we have something that learns continuously from more input, I am not impressed
It is only a limitation of the interface that we're interacting with. There is no reason it couldn't backpropagate towards a better solution when told that it's wrong. OpenAI probably aren't letting it train online lest some jokers try to teach it racism and other bullshit etc.
That is a lot of handwaving/massive oversimplification - there are a number of reasons this is infeasible (one already mentioned) It matters because a lot of the AI hype these days relies in part on people’s misunderstanding of this.
Earlier quoted context omitted.
GPT3 can't create ASCII art for shit either. Though it can make little ASCII tables of data.
I asked it to create an ASCII art banana and the result was hilarious. It then tried to explain it by elaborating that the 'O' was a curvy letter and represented the curves of the banana.
Earlier quoted context omitted.
Think of it as overly aggressive error correction at the language level. It has a context of some Base64 code. Given that is almost always seen associated with computer code, is "address" or "actress" more likely. It "knows" the algorithm for decoding base64, and can follow those steps. But it can't overcome it's built-in biases for optimizing the most likely output given the context. (This problem is solvable, but I…
> Given that is almost always seen associated with computer code, is "address" or "actress" more likely. Sorry, but I don't buy it. I don't think "address" is a particularly likely word to appear in code, especially the kind of code that uses base64 (usually high-level). It appears even less often inside base64 encoded content.
The original use for base64 was to send binary content to an email address.
Related to this: I had fun the other night trying to explain rhymes to ChatGPT. It could ONLY write rhyming couplets, and even when I explained exactly which sentences in a poem I wanted to rhyme, it would write a couplet. (That even happened sometimes when I asked it specifically NOT to rhyme). Eventually I got it to manage ABAB rhymes by: 1. Asking it to generate four sentences on a topic with the same meter and nu…
https://i.imgur.com/MHxjVHy.png
I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.
/
V2h5IGRpZCB0aGUgY2hpY2tlbiBjcm9zcyB0aGUgcm9hZD8=
Why did the chicken cross the road?I find it amusing that, at present, ChatGPT seems to be lousy at mathematical-type reasoning while being very good at natural language use. That is the opposite of what many people, including me, have come to expect of computers. I have worked for many years in translation, lexicography, and language education, and I am flabbergasted at how well ChatGPT handles natural language. It can produce example sentences of po…
Only thanks to seeing numbers in vast amount of text it was trained on, it is able to do common math relatively well, and anything uncommon very poorly.
I find it amusing that, at present, ChatGPT seems to be lousy at mathematical-type reasoning while being very good at natural language use. That is the opposite of what many people, including me, have come to expect of computers. I have worked for many years in translation, lexicography, and language education, and I am flabbergasted at how well ChatGPT handles natural language. It can produce example sentences of po…
If can do math by hand using rules it knows until it spits out a numeral form - then it errors.