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I don't find this argument persuasive. If you need to engineer the prompt in order for the model to give you the answer you want, and if it's performing poorly

I don't find this argument persuasive. If you need to engineer the prompt in order for the model to give you the answer you want, and if it's performing poorly without that kind of assistance from a human who understands the task and the desired outcome... then it's perfectly OK to not bother and give the LLM a low mark on a benchmark. After all, the whole point was to test the LLM's language and reasoning skills. "Prompt engineering" makes sense if your goal is more utilitarian, but it's not a license to cheat on tests. If you want a machine learning model to generate aesthetically pleasing images, or to follow certain rules in a conversation, then it's OK to rely on hacky solutions to get there.

famouswaffles 3y ago
I kind of dislike the term "Prompt Engineering". It mystifies what to me is an ordinary process. I'm not talking about chanting magic words, i'm just talking about taking the structure of an LLM into account, even taking how humans would approach the problem into account. In microsoft's agi paper, there is a test for planning they put out for GPT-4. They give detailed instructions on the constraint of a poem and expect it to spit out the constrained poem in one shot. Of course it fails and the conclusion is that it can't plan. But if you think about it, this is a ridiculous assertion. No human on earth would have passed that test with working memory alone. Even by our standards, it's a weird way to present the information but they did so anyway. This is actually a major issue for most plan benchmark assessment for LLMs i've come across. These are the kind of problems i'm talking about. If i changed the request to encourage for a draft/revise generation process and it consistently passed these tests then it's very fair to say it can plan. That's not "hacky". It's just truth. and saying otherwise is denying real world usage for a misplaced sense of propriety to benchmarks. a benchmark is only as useful as the capabilities it can assess. a "this is how this is presented, this is how the model performed" would have been very prudent in my opinion. Maybe these guys already covered the kind of things i'm talking about(i can accept that!)....but i can't know that if they don't tell us.
- haskellandchill 3y ago
  how is "Engineering" "about chanting magic words"? The wires may be getting crossed on if it's an engineering discipline or just the use of the word engineer, meaning to build mindfully, I feel the latter is appropriate and the former more misguided but still it's not "Prompt Wizarding" ;)
  - famouswaffles 3y ago
    Fair Enough. Just that discussion around the term feels like that sometimes especially with image generation.
  - Guthur 3y ago
    Well you're building nothing, your poking at a black box and hoping it spits out something close to what you want.
    - pietro72ohboy 3y ago
      Precisely! You’re trying to learn the art of poking the box in the right way. As an engineer, I really despise the bastardisation of the term to refer to jobs or tasks that are inherently non-scientific.
      - elforce002 3y ago
        I agree with you a 100%
        haskellandchill 3y ago
        It can be done scientifically, there are many techniques in the literature that put the engineering into Prompt Engineering.
altruios 3y ago
So what if we have an AI prompter-assister... we take an input prompt: run it through an AI to transform that prompt into proper AI talk - then that is what's prompted to the actual AI... That way the AI will always have the best prompt possible, because the Prompt-helper helps your prompt... /s if anyone needs it.
- larperdoodle 3y ago
  Is the /s because that's literally what they're already doing?
viraptor 3y ago
I think adjusting the prompt makes sense. We have to keep in mind that this implicitly happens on the human side as well. The person creating the initial question will consider how understandable it is for them and edit until they're happy. Then the questions will be reviewed by co-authors and possibly tested on a few people before running the full experiment. They'd probably review the explanation again or change the task if they realised half the people can't follow the instructions. We just call it "editing for clarity" rather than "prompt engineering" when we write for other people.
nomel 3y ago
I would claim that most people are very bad at communicating in a way that can be interpreted, by human or AI, with a single shot. Most of the time some back and forth is required to clear things up. I think it's unfair to expect gold from garbage input, since meat brains can't do that either, without some back and fourth. Those prompts should be available. It's ridiculous that they're not.
quitit 3y ago
While I think that's a valid point, for the purpose of the paper having a competent prompt writer would be needed to make a fair comparison. I think the researchers would know how to get the best from their own AI bot, so that's a level of competency that should be extended to comparisons, otherwise user competency becomes a source of bias. I do feel you're correct in your concerns though, the systems shouldn't need experts to use them, nor should they need the user to already know the right answer, which leads me to my next point: When it comes to real world expectations, perhaps instead we need a large group of random people (with no prior experience) working with each bot to complete a set of tasks in order to determine how it truly performs - something that could be enhanced if the answers weren't easy to check.
emtel 3y ago
Disagree - a model may have capabilities that it only deploys in certain circumstances. In particular, if you train on the whole internet, you probably learn what both stupid and smart outputs look like. But you probably also learn that the modal output is stupid. So it’s no ding on the model’s capabilities if it defaults to assuming it should behave stupidly.
- PumpkinSpice 3y ago
  What you're basically saying is "the model as trained can't do well at this task, so let's use our own cognitive skills to help it." That's a problem for comparative benchmarking, right? You're no longer testing the model; you're testing the model in tandem with the prompt engineer. This raises several big questions: 1) How do you know that the engineer's knowledge of the "correct" answer isn't being subtly encoded in the prompt? Essentially the Clever Hans phenomenon. 2) If you want this to be a fair game, how do you give precisely the same kind of advantage to whatever you're benchmarking the LLM against? 3) Last but not least, if you're not going to throw in your prompt engineer for free with the product, will your results be reproducible by your customers? To be clear, I don't think there's some cosmically objective way of doing this. If you're using prompts written for humans, you're already putting the computer at a disadvantage. But at the very least, you're measuring something meaningful: how will the model behave in the real world.

famouswaffles 3y ago

I kind of dislike the term "Prompt Engineering". It mystifies what to me is an ordinary process. I'm not talking about chanting magic words, i'm just talking about taking the structure of an LLM into account, even taking how humans would approach the problem into account. In microsoft's agi paper, there is a test for planning they put out for GPT-4. They give detailed instructions on the constraint of a poem and expect it to spit out the constrained poem in one shot. Of course it fails and the conclusion is that it can't plan. But if you think about it, this is a ridiculous assertion. No human on earth would have passed that test with working memory alone. Even by our standards, it's a weird way to present the information but they did so anyway. This is actually a major issue for most plan benchmark assessment for LLMs i've come across. These are the kind of problems i'm talking about. If i changed the request to encourage for a draft/revise generation process and it consistently passed these tests then it's very fair to say it can plan. That's not "hacky". It's just truth. and saying otherwise is denying real world usage for a misplaced sense of propriety to benchmarks. a benchmark is only as useful as the capabilities it can assess. a "this is how this is presented, this is how the model performed" would have been very prudent in my opinion. Maybe these guys already covered the kind of things i'm talking about(i can accept that!)....but i can't know that if they don't tell us.

haskellandchill 3y ago
how is "Engineering" "about chanting magic words"? The wires may be getting crossed on if it's an engineering discipline or just the use of the word engineer, meaning to build mindfully, I feel the latter is appropriate and the former more misguided but still it's not "Prompt Wizarding" ;)
- famouswaffles 3y ago
  Fair Enough. Just that discussion around the term feels like that sometimes especially with image generation.
- Guthur 3y ago
  Well you're building nothing, your poking at a black box and hoping it spits out something close to what you want.
  - pietro72ohboy 3y ago
    Precisely! You’re trying to learn the art of poking the box in the right way. As an engineer, I really despise the bastardisation of the term to refer to jobs or tasks that are inherently non-scientific.
    - elforce002 3y ago
      I agree with you a 100%
      - haskellandchill 3y ago
        It can be done scientifically, there are many techniques in the literature that put the engineering into Prompt Engineering.

altruios 3y ago

So what if we have an AI prompter-assister... we take an input prompt: run it through an AI to transform that prompt into proper AI talk - then that is what's prompted to the actual AI... That way the AI will always have the best prompt possible, because the Prompt-helper helps your prompt... /s if anyone needs it.

larperdoodle 3y ago
Is the /s because that's literally what they're already doing?

viraptor 3y ago

I think adjusting the prompt makes sense. We have to keep in mind that this implicitly happens on the human side as well. The person creating the initial question will consider how understandable it is for them and edit until they're happy. Then the questions will be reviewed by co-authors and possibly tested on a few people before running the full experiment. They'd probably review the explanation again or change the task if they realised half the people can't follow the instructions. We just call it "editing for clarity" rather than "prompt engineering" when we write for other people.

nomel 3y ago

I would claim that most people are very bad at communicating in a way that can be interpreted, by human or AI, with a single shot. Most of the time some back and forth is required to clear things up. I think it's unfair to expect gold from garbage input, since meat brains can't do that either, without some back and fourth. Those prompts should be available. It's ridiculous that they're not.

quitit 3y ago

While I think that's a valid point, for the purpose of the paper having a competent prompt writer would be needed to make a fair comparison. I think the researchers would know how to get the best from their own AI bot, so that's a level of competency that should be extended to comparisons, otherwise user competency becomes a source of bias. I do feel you're correct in your concerns though, the systems shouldn't need experts to use them, nor should they need the user to already know the right answer, which leads me to my next point: When it comes to real world expectations, perhaps instead we need a large group of random people (with no prior experience) working with each bot to complete a set of tasks in order to determine how it truly performs - something that could be enhanced if the answers weren't easy to check.

emtel 3y ago

Disagree - a model may have capabilities that it only deploys in certain circumstances. In particular, if you train on the whole internet, you probably learn what both stupid and smart outputs look like. But you probably also learn that the modal output is stupid. So it’s no ding on the model’s capabilities if it defaults to assuming it should behave stupidly.

PumpkinSpice 3y ago
What you're basically saying is "the model as trained can't do well at this task, so let's use our own cognitive skills to help it." That's a problem for comparative benchmarking, right? You're no longer testing the model; you're testing the model in tandem with the prompt engineer. This raises several big questions: 1) How do you know that the engineer's knowledge of the "correct" answer isn't being subtly encoded in the prompt? Essentially the Clever Hans phenomenon. 2) If you want this to be a fair game, how do you give precisely the same kind of advantage to whatever you're benchmarking the LLM against? 3) Last but not least, if you're not going to throw in your prompt engineer for free with the product, will your results be reproducible by your customers? To be clear, I don't think there's some cosmically objective way of doing this. If you're using prompts written for humans, you're already putting the computer at a disadvantage. But at the very least, you're measuring something meaningful: how will the model behave in the real world.