61–70 of 75 posts
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Even if you don't consider an Italian word as a word: It's a last name. It's a brand name. It is several companies' name. It belongs in the list just fine.
It seems you agree with me, I do not understand. Wrong thread maybe you replied to?
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This needs citation. These are not the same things. It will get numerical references right if it has sources used in the model, but it isn't doing any numerical calculations.
I can feed ChatGPT code that does calculations (and have) and have it calculate the right answers. It also gets it wrong a lot, so it's not good at that, but any notion that it can't do numerical calculations is easy to disprove.
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It's true that ChatGPT is not designed for counting and struggles with it in general. But my point was that ChatGPT, like any tokenized LLM, doesn't even have the concept of letters. The prompt "how many e's in this sentence" is rendered as the tokens [4919, 867, 304, 338, 287, 428, 6827]. There just isn't a pathway for it to consider the letters that make up those tokens. I'm a little surprised it did that well on y…
In a prompt, can you just tell the model which letters make up each token? Eg a list of ag = a g etc. I imagine a dictionary of that for all tokens in the training data would help.
Intuitively I think there's an abstraction barrier there, but I'm not positive. It feels like asking us to list all of the words that trigger particular neurons.
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Aren't you Greg Diamos, the founder, why are you asking this instead of answering?
This was a frequently asked question among my friends. I’m really curious to see how someone who hasn’t been staring at the docs for weeks would explain it.
This is a question you should instead ask in a user interview, or at a minimum qualify when asking here as one of the people involved in the project.
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I've had ChatGPT build a fire nctioning website, write a DNA server, fill in significant portions of specs, all without the problems you describe. I'm never going back to doing things from scratch - it's saving me immense amounts of time every single day. The only reasonable conclusion is that the way you're promoting it is counterproductive.
Good thing then that I specifically mentioned gpt as being able to follow instruction and that I was specifically mentioning the other models. You're welcome to demonstrate the same ability on other models tho.
And I mentioned ChatGPT because from context of your comments here it was unclear on first read-through what you meant. Maybe consider that it's possible your prompting is not geared for the models you've tried.
Not least, specifically given that if you expect a model to know how to follow instructions, when most of them have not been through RLHF you're using them wrong. A lot of them needs prompt shaped as a completion, not a conversation.
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well, here we go, ChatGPT in GPT-4 mode: There are 7 instances of the letter 'e' in the sentence: "Try asking chatGPT to count how many e's are in a sentence." another one: The words with the letter 'e' from the sentence "Try asking chatGPT to count how many e's are in a sentence" are: asking sentence and another, notice the last one: Here are some English words containing three instances of the letter 'e': Neverthel…
On the last list, the only word that does not comply with the constraints (having 3 'e's) is "Demeanor", which has only 2. Not great but also not as horrible as you make it sound.
Trivial examples show that this isn't nearly as good as ChatGPT. The headline should be changed.
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Good thing then that I specifically mentioned gpt as being able to follow instruction and that I was specifically mentioning the other models. You're welcome to demonstrate the same ability on other models tho.
You can get useful results out of a whole lot of them as long as you actually prompt them in a way suitable for the models. The point I made originally was that if you just feed them an ambiguous question, then sure, you will get extremely variable and mostly useless results out. Ironically, And I mentioned ChatGPT because from context of your comments here it was unclear on first read-through what you meant. Maybe c…
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I haven't done this with sentence transformers but I imagine it's possible since they can be loaded as regular transformers. Check out https://github.com/huggingface/peft -- they've packaged it up nicely- and read up on LoRA ( https://arxiv.org/pdf/2106.09685.pdf ) That should get you started.
Thank you. Peft and adapters seem to be two different things though, no? AFAIK there are other libraries for adapters (forgot the name). Is peft what you were talking about when you said adapters in your original comment?
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To be fair the question did not specify the language and included a disclaimer about it.
Even if you don't consider an Italian word as a word: It's a last name. It's a brand name. It is several companies' name. It belongs in the list just fine.
The strength of LLM is their ability to answer to unprecisely specified questions, being able to guess the speaker's intent, but in this particular case, it's failing the test.