5 ms·

> people do the same thing You're right, most people do that, but that's because they haven't trained themselves to be inventors. This is a skill that definite

> people do the same thing You're right, most people do that, but that's because they haven't trained themselves to be inventors. This is a skill that definitely needs to be developed more, but finding truly creative (and sometimes backwards-seeming) solutions can sometimes mean thinking about problems in a completely different way. However, all LLM's do this. That's one of the points of the article.

refulgentis 2y ago
There was an actual peer-reviewed article from an institution yesterday showing, numerically, robustly, human evaluated, that LLMs are better at creative thinking than humans. I don't think handwaving about exclusively outputting training data and random punditry via article really holds up here. It's wrong for so many reasons, from the training data having more perspectives, to the "Fat Tony" test, empirically, we can go make it output novel combinations right now, to the CS test, we can have it emulate arbitrary Turing machines.
- wrycoder 2y ago
  Might you possibly have a link?
  - refulgentis 2y ago
    My bad, looked it up and meant to edit it in, dunno what happened: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10858891/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10858891/
    - barfbagginus 2y ago
      The methodology of this paper is seriously suspect. First they have a very small sample size of around n = 150. Second, they don't represent average husband, they use people working on mechanical Turk for $9 an hour. Finally, they don't have any standard measures of divergent thought - they just use 4 different types of question answering problems, and those are supposed to measure creativity. Whether they do or don't would require us to evaluate the questions. It's important to be clear that this is saying that LLMs might be more creative than low paid workers on mechanical Turk, given no training time. That's not a very good representation of the average human, or the fact that humans need training. So for example, an average doctor or scientist might still be more creative than LLMs. Tldr; this is not a good paper to throw around if you want to convince people. It's truly awful in its design, and the way it's presented is not always honest.
      - refulgentis 2y ago
        No results for mechanical turk, n=151 is perfectly significant for what amounts to a taste test (I was quite taken aback to read that, 151 is low in very few contexts), I'm not "throwing it around", whatever that means in this context, and the extreme reaction and 100% focus on it belies that it was 1 of 3 short points I made, as well as that the research backs up the claim that was shut down, that is intuitively correct, I'd rather have a 1000 specialists at undergrad level ideating than me and Einstein and Picasso and Steve Jobs. So, yeah, between that, and that both your average person and someone arguing from Turing tapes can show AI doing things not in the training data, I'm going with actual data thats being "thrown around" rather than the rando blog post that argues from "by definition it can't be creative because it isn't original because it only repeats training data" Call me when they're at N=152 on a blind test.
      - rafaeljimenez 2y ago
        They also don’t seem to be taking into consideration the fact that answers to those tests may already be present in the training data.
- imtringued 2y ago
  There is a difference between a verbatim answer and an answer in the shape of a verbatim answer. The latter is not much better if you actually want to do new things.
dragonwriter 2y ago
> However, all LLM’s do this. LLM’s degree of adherence to training patterns can be directly tuned, per request (up to whatever the maximum it is capable of.) Can this be leveraged to build a framework around an LLM for brainstorming usefully? I’m not sure. But “Nope, because LLMs just directly adhere to existing patterns” is definitely not a useful answer to that question. It is the kind of answer someone who just parrots common patterns with surface-level knowledge of the subject area would come up with. (Huh, did a default-settings LLM write this article?)
- little_name 2y ago
  > LLM’s degree of adherence to training patterns can be directly tuned, per request (up to whatever the maximum it is capable of.) I think people often forget the behavior of instruction following is still based on SFT training data. This is exactly "adherence to training patterns". Prompt engineering is not elixir, it's just a way to utilize the patterns seen in training data.
  - dragonwriter 2y ago
    > Prompt engineering is not elixir I’m not talking about prompt engineering when I talk about per request tuning of how closely it follows established patterns, I’m talking about inference parameters (temperature, top_p, top_k, are common for most models and ways of calling them, some others may be available, too.)
    - deleted 2y ago
      [deleted]
    - little_name 2y ago
      There is a thread below discussing tuning temperature and why it's not tied to creativity. I think the point is temperature and other sampling parameters don't have any inherent mechanism to control the adherence to training pattern as a whole (all top-X candidates are all from training data). Deviating from the max probability in next token generation has no relation with thinking out of box. It does give more generation results that may be lucky to hit something, but I doubt the cost effectiveness

refulgentis 2y ago

There was an actual peer-reviewed article from an institution yesterday showing, numerically, robustly, human evaluated, that LLMs are better at creative thinking than humans. I don't think handwaving about exclusively outputting training data and random punditry via article really holds up here. It's wrong for so many reasons, from the training data having more perspectives, to the "Fat Tony" test, empirically, we can go make it output novel combinations right now, to the CS test, we can have it emulate arbitrary Turing machines.

wrycoder 2y ago
Might you possibly have a link?
- refulgentis 2y ago
  My bad, looked it up and meant to edit it in, dunno what happened: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10858891/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10858891/
  - barfbagginus 2y ago
    The methodology of this paper is seriously suspect. First they have a very small sample size of around n = 150. Second, they don't represent average husband, they use people working on mechanical Turk for $9 an hour. Finally, they don't have any standard measures of divergent thought - they just use 4 different types of question answering problems, and those are supposed to measure creativity. Whether they do or don't would require us to evaluate the questions. It's important to be clear that this is saying that LLMs might be more creative than low paid workers on mechanical Turk, given no training time. That's not a very good representation of the average human, or the fact that humans need training. So for example, an average doctor or scientist might still be more creative than LLMs. Tldr; this is not a good paper to throw around if you want to convince people. It's truly awful in its design, and the way it's presented is not always honest.
    - refulgentis 2y ago
      No results for mechanical turk, n=151 is perfectly significant for what amounts to a taste test (I was quite taken aback to read that, 151 is low in very few contexts), I'm not "throwing it around", whatever that means in this context, and the extreme reaction and 100% focus on it belies that it was 1 of 3 short points I made, as well as that the research backs up the claim that was shut down, that is intuitively correct, I'd rather have a 1000 specialists at undergrad level ideating than me and Einstein and Picasso and Steve Jobs. So, yeah, between that, and that both your average person and someone arguing from Turing tapes can show AI doing things not in the training data, I'm going with actual data thats being "thrown around" rather than the rando blog post that argues from "by definition it can't be creative because it isn't original because it only repeats training data" Call me when they're at N=152 on a blind test.
    - rafaeljimenez 2y ago
      They also don’t seem to be taking into consideration the fact that answers to those tests may already be present in the training data.
imtringued 2y ago
There is a difference between a verbatim answer and an answer in the shape of a verbatim answer. The latter is not much better if you actually want to do new things.

dragonwriter 2y ago

> However, all LLM’s do this. LLM’s degree of adherence to training patterns can be directly tuned, per request (up to whatever the maximum it is capable of.) Can this be leveraged to build a framework around an LLM for brainstorming usefully? I’m not sure. But “Nope, because LLMs just directly adhere to existing patterns” is definitely not a useful answer to that question. It is the kind of answer someone who just parrots common patterns with surface-level knowledge of the subject area would come up with. (Huh, did a default-settings LLM write this article?)

little_name 2y ago
> LLM’s degree of adherence to training patterns can be directly tuned, per request (up to whatever the maximum it is capable of.) I think people often forget the behavior of instruction following is still based on SFT training data. This is exactly "adherence to training patterns". Prompt engineering is not elixir, it's just a way to utilize the patterns seen in training data.
- dragonwriter 2y ago
  > Prompt engineering is not elixir I’m not talking about prompt engineering when I talk about per request tuning of how closely it follows established patterns, I’m talking about inference parameters (temperature, top_p, top_k, are common for most models and ways of calling them, some others may be available, too.)
  - deleted 2y ago
    [deleted]
  - little_name 2y ago
    There is a thread below discussing tuning temperature and why it's not tied to creativity. I think the point is temperature and other sampling parameters don't have any inherent mechanism to control the adherence to training pattern as a whole (all top-X candidates are all from training data). Deviating from the max probability in next token generation has no relation with thinking out of box. It does give more generation results that may be lucky to hit something, but I doubt the cost effectiveness