4 ms·

>I feel a quality prompt engineer could beat 85% with GPT4. I generally don't pull the "you're not prompting it good enough" card unless i have direct experien

>I feel a quality prompt engineer could beat 85% with GPT4. I generally don't pull the "you're not prompting it good enough" card unless i have direct experience with the task but this does raise a few flags - "between 42 and 86% of the time, depending on how the researchers presented the task." I would have liked them to show how they presented this task, at least something more than a single throw away line, given how integral it is to the main claim.

randcraw 3y ago
The article suggested that GPF-4 FAILED between 42 and 86 percent of the time. So its success rate would be 58 to 14 percent, which compared to the novel NN's 80 percent, seems significant.
- famouswaffles 3y ago
  Yes I'm aware. My point is that 1. The level of variance displayed here is very atypical for a language model unless you're making significant changes to how the information is presented. This alone is cause for more elaboration. 2. With this big a variance already showing, it's hard to say for sure that they hit the top end. Maybe it seems strange but most LLM evaluation papers still don't actually bother to tie in even some extremely well known/basic "output boosters" like chain of thought and so on. But at least in these instances, we know how they presented it. a "this is how we presented the task, this is how it performed" would have been very nice.
PumpkinSpice 3y ago
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.
vessenes 3y ago
Agreed. And there's going to be some significant domain decisions for GPT-4 to consider: Will the made up words be existing single tokens? Will those single tokens be heavily overloaded tokens, e.g. a letter of the alphabet? Will the representation of the circles be, say "B" for blue, or something else? Along with this are questions as to whether you're going to treat GPT-4 as a zero shot or multi-shot oracle; while they have this idea of 'context free' challenges in the paper, they, crucially, train their network first. Anyway, I like this paper less the more I think about it.
pama 3y ago
The supplement discusses how they presented the task. Notably, they first gave all the training examples and then told the model what the task is and didn't ask it to reason or create any context before spitting out the answers. So basically the simplest way to interact with it, but probably not a great way to get solutions for this problem if that was the task at hand. https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-023-06668-3/MediaObjects/41586_2023_6668_MOESM1_ESM.pdf https://static-content.springer.com/esm/art%3A10.1038%2Fs415...

randcraw 3y ago

The article suggested that GPF-4 FAILED between 42 and 86 percent of the time. So its success rate would be 58 to 14 percent, which compared to the novel NN's 80 percent, seems significant.

famouswaffles 3y ago
Yes I'm aware. My point is that 1. The level of variance displayed here is very atypical for a language model unless you're making significant changes to how the information is presented. This alone is cause for more elaboration. 2. With this big a variance already showing, it's hard to say for sure that they hit the top end. Maybe it seems strange but most LLM evaluation papers still don't actually bother to tie in even some extremely well known/basic "output boosters" like chain of thought and so on. But at least in these instances, we know how they presented it. a "this is how we presented the task, this is how it performed" would have been very nice.

PumpkinSpice 3y ago

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.

vessenes 3y ago

Agreed. And there's going to be some significant domain decisions for GPT-4 to consider: Will the made up words be existing single tokens? Will those single tokens be heavily overloaded tokens, e.g. a letter of the alphabet? Will the representation of the circles be, say "B" for blue, or something else? Along with this are questions as to whether you're going to treat GPT-4 as a zero shot or multi-shot oracle; while they have this idea of 'context free' challenges in the paper, they, crucially, train their network first. Anyway, I like this paper less the more I think about it.

pama 3y ago

The supplement discusses how they presented the task. Notably, they first gave all the training examples and then told the model what the task is and didn't ask it to reason or create any context before spitting out the answers. So basically the simplest way to interact with it, but probably not a great way to get solutions for this problem if that was the task at hand. https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-023-06668-3/MediaObjects/41586_2023_6668_MOESM1_ESM.pdf https://static-content.springer.com/esm/art%3A10.1038%2Fs415...