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I asked GPT-4o and I got the correct answer in one shot: > Only one Monty Python member, Michael Palin, has been knighted. He was honored in 2019 for his contr

by gizmo 2y ago

I asked GPT-4o and I got the correct answer in one shot: > Only one Monty Python member, Michael Palin, has been knighted. He was honored in 2019 for his contributions to travel, culture, and geography. His extensive work as a travel documentarian, including notable series on the BBC, earned him recognition beyond his comedic career with Monty Python (NERDBOT) (Wikipedia). > Other members, such as John Cleese, declined honors, including a CBE (Commander of the British Empire) in 1996 and a peerage later on (8days). Maybe you just asked the question wrong. My prompt was "which monty python actors have been knighted. look it up and give the reasons why. be brief".

deleted 2y ago
[deleted]
latexr 2y ago
Yes yes, there’s always some “you're holding it wrong” apologist.¹ Look, it’s not a complicated question to ask unambiguously. If you understand even a tiny bit of how these models work, you know you can make the exact same question twice in a row and get wildly different answers. The point is that you never know what you can trust or not. Unless you’re intimately familiar with Monty Python history, you only know you got the correct answer in one shot because I already told you what the right answer is. Oh, and by the way, I just asked GPT-4o the same question, with your phrasing, copied verbatim and it said two Pythons were knighted: Michael Palin (with the correct reasons this time) and John Cleese. ¹ And I’ve had enough discussions on HN where someone insists on the correct way to prompt, then they do it and get wrong answers. Which they don’t realise until they shared it and disproven their own argument.
- oblio 2y ago
  Unless I'm mistaken, isn't all the math behind them... ultimately probabilistic? Even theoretically they can't guarantee the same answer. I'm agreeing with you, by the way, just curious if I'm missing something.
  - gizmo 2y ago
    If you take a photo the photons hitting the camera sensor do so in a probabilistic fashion. Still, in sufficient light you'll get the same picture every time you press the shutter button. In near darkness you'll get a random noise picture every time. Similarly language models are probabilistic and yet they get the easiest questions right 100% of the time with little variability and the hardest prompts will return gibberish. The point of good prompting is to get useful responses to questions at the boundary of what the language model is capable of. (You can also configure a language model to generate the same output for every prompt without any random noise. Image models for instance generate exactly the same image pixel for pixel when given the same seed.)
    - latexr 2y ago
      The photo comparison is disingenuous. Light and colour information can be disorganised to a large extent and yet you still perceive the same from an image. You can grab a photo and apply to it a red filter or make it black and white and still understand what’s in there, what it means, and how it compares to reality. In comparison, with text a single word can change the entire meaning of a sentence, paragraph, or idea. The same word in different parts of a text can make all the difference between clarity and ambiguity. It makes no difference how good your prompting is, some things are simply unknowable by an LLM. I repeatedly asked GPT-4o how many Magic: The Gathering cards based on Monty Python exist. It said there are none (wrong) because they didn’t exist yet at the cut off date of its training. No amount of prompting changes that, unless you steer it by giving it the answer (at which point there would have been no point in asking). Furthermore, there’s no seed that guarantees truth in all answers or the best images in all cases. Seeds matter for reproducibility, they are unrelated to accuracy.
      - gizmo 2y ago
        Language is fuzzy in exactly the same way. LLMs can create factually correct responses in dozens of languages using endless variations in phrasing. You fixate on the kind of questions that current language models struggle with but you forget that for millions of easier questions modern language models already respond with a perfect answer every time. You think the probabilistic nature of language models is a fundamental problem that puts a ceiling on how smart they can become, but you're wrong.
- gizmo 2y ago
  I think your iPhone analogy is apt. Do you want to be the person complaining that the phone drops calls or do you want to hold it slightly differently and get a lot of use out of it? If you pay careful attention to prompt phrasing you will get a lot more mileage out of these models. That's the bottom line. If you believe that you shouldn't have to learn how to use a tool well then you can be satisfied with your righteous attitude but you won't get anywhere.
  - latexr 2y ago
    No one’s arguing that correct use of a tool isn’t beneficial. The point is that insisting LLMs just need good prompting is delusional and a denial of reality. I have just demonstrated how your own prompt is still capable of producing the wrong result. So either you don’t know how to prompt correctly (because if you did, by your own logic it would have produced the right response every time, which it didn’t) or the notion that all you need is good prompting is wrong. Which anyone who understands the first thing about these systems knows to be the case.

by gizmo 2y ago

deleted 2y ago

[deleted]

latexr 2y ago

Yes yes, there’s always some “you're holding it wrong” apologist.¹ Look, it’s not a complicated question to ask unambiguously. If you understand even a tiny bit of how these models work, you know you can make the exact same question twice in a row and get wildly different answers. The point is that you never know what you can trust or not. Unless you’re intimately familiar with Monty Python history, you only know you got the correct answer in one shot because I already told you what the right answer is. Oh, and by the way, I just asked GPT-4o the same question, with your phrasing, copied verbatim and it said two Pythons were knighted: Michael Palin (with the correct reasons this time) and John Cleese. ¹ And I’ve had enough discussions on HN where someone insists on the correct way to prompt, then they do it and get wrong answers. Which they don’t realise until they shared it and disproven their own argument.

oblio 2y ago
Unless I'm mistaken, isn't all the math behind them... ultimately probabilistic? Even theoretically they can't guarantee the same answer. I'm agreeing with you, by the way, just curious if I'm missing something.
- gizmo 2y ago
  If you take a photo the photons hitting the camera sensor do so in a probabilistic fashion. Still, in sufficient light you'll get the same picture every time you press the shutter button. In near darkness you'll get a random noise picture every time. Similarly language models are probabilistic and yet they get the easiest questions right 100% of the time with little variability and the hardest prompts will return gibberish. The point of good prompting is to get useful responses to questions at the boundary of what the language model is capable of. (You can also configure a language model to generate the same output for every prompt without any random noise. Image models for instance generate exactly the same image pixel for pixel when given the same seed.)
  - latexr 2y ago
    The photo comparison is disingenuous. Light and colour information can be disorganised to a large extent and yet you still perceive the same from an image. You can grab a photo and apply to it a red filter or make it black and white and still understand what’s in there, what it means, and how it compares to reality. In comparison, with text a single word can change the entire meaning of a sentence, paragraph, or idea. The same word in different parts of a text can make all the difference between clarity and ambiguity. It makes no difference how good your prompting is, some things are simply unknowable by an LLM. I repeatedly asked GPT-4o how many Magic: The Gathering cards based on Monty Python exist. It said there are none (wrong) because they didn’t exist yet at the cut off date of its training. No amount of prompting changes that, unless you steer it by giving it the answer (at which point there would have been no point in asking). Furthermore, there’s no seed that guarantees truth in all answers or the best images in all cases. Seeds matter for reproducibility, they are unrelated to accuracy.
    - gizmo 2y ago
      Language is fuzzy in exactly the same way. LLMs can create factually correct responses in dozens of languages using endless variations in phrasing. You fixate on the kind of questions that current language models struggle with but you forget that for millions of easier questions modern language models already respond with a perfect answer every time. You think the probabilistic nature of language models is a fundamental problem that puts a ceiling on how smart they can become, but you're wrong.
gizmo 2y ago
I think your iPhone analogy is apt. Do you want to be the person complaining that the phone drops calls or do you want to hold it slightly differently and get a lot of use out of it? If you pay careful attention to prompt phrasing you will get a lot more mileage out of these models. That's the bottom line. If you believe that you shouldn't have to learn how to use a tool well then you can be satisfied with your righteous attitude but you won't get anywhere.
- latexr 2y ago
  No one’s arguing that correct use of a tool isn’t beneficial. The point is that insisting LLMs just need good prompting is delusional and a denial of reality. I have just demonstrated how your own prompt is still capable of producing the wrong result. So either you don’t know how to prompt correctly (because if you did, by your own logic it would have produced the right response every time, which it didn’t) or the notion that all you need is good prompting is wrong. Which anyone who understands the first thing about these systems knows to be the case.