5 ms·

I’m not fond of the provocative title because prompting is easy and only getting easier; the advice seems to be predicated on the use of relatively deficient LL

I’m not fond of the provocative title because prompting is easy and only getting easier; the advice seems to be predicated on the use of relatively deficient LLMs. I don’t doubt there will still be operator skill involved, but I anticipate the state of the art for LLMs ability to adapt to “bad” prompts will outpace our ability to learn to prompt them effectively. Disclaimer: I watched the video but didn’t read the paper.

domoritz 3y ago
I think there are a lot of instances where writing prompts can be hard just because it’s hard to express your needs in words sometimes. Bad prompts are often ambiguous and there is only so much even a perfect LLM can correct for. That is, until we have direct connections to our brains.
- mellavora 3y ago
  What makes you think it is clearer in your brain than it is in your words? My experience is that in my case it isn't.
  - pbronez 3y ago
    Agree. Writing is a necessary part of clear though.
  - riwsky 3y ago
    Who said we’d only give the LLMs read access?
version_five 3y ago
I think you're right about prompts getting "easier" but I don't think it's a good thing. I expect it will evolve like google search. Where initially there are ways to increase specificity, or at least introduce enough randomness to get some different results, it will converge to something that ignores most of what you prompt and gives you what OpenAI wants you to see. That's really the only way adapting to "bad" prompts even could work
zamfi 3y ago
LLMs will definitely get better -- and people will adapt too. But natural language contains inherent ambiguity, and that's not going to change. A large part of the paper talks about how the challenges of programming and developing ML systems don't go away just because you're using natural language and an LLM. For example, users incorrectly extrapolating from a single failed/successful prompt to other contexts: that's not something that will be solved by better LLMs, really. It's solved by getting users to recognize when they do/don't have enough data to believe what they're seeing. Disclaimer: I wrote the paper.
- digdugdirk 3y ago
  Thanks for the official TL/DR. ... And the laughs. Paper is great, too. What's it like trying to write a paper while the field is shifting so rapidly? Is there anything you would have liked to include that had to be cut so you could get it out the door?
  - zamfi 3y ago
    Oh, that's not a full TL;DR -- but sounds like you've read the paper. :) Shifting field: yes, we spend a lot of time trying to figure out what is going to change and what isn't, at least in HCI (my field). LLM capabilities are changing by the...day? So anything that's very focused on LLM capabilities won't be relevant for long. Humans don't change quite as fast! Keep in mind this paper was first written in September 2022, reflecting work done prior to then...practically an eternity in which we've since seen ChatGPT take the world by storm, and now GPT-4. But I think the lessons there are still very relevant, and will be for quite some time.

domoritz 3y ago

I think there are a lot of instances where writing prompts can be hard just because it’s hard to express your needs in words sometimes. Bad prompts are often ambiguous and there is only so much even a perfect LLM can correct for. That is, until we have direct connections to our brains.

mellavora 3y ago
What makes you think it is clearer in your brain than it is in your words? My experience is that in my case it isn't.
- pbronez 3y ago
  Agree. Writing is a necessary part of clear though.
- riwsky 3y ago
  Who said we’d only give the LLMs read access?

version_five 3y ago

I think you're right about prompts getting "easier" but I don't think it's a good thing. I expect it will evolve like google search. Where initially there are ways to increase specificity, or at least introduce enough randomness to get some different results, it will converge to something that ignores most of what you prompt and gives you what OpenAI wants you to see. That's really the only way adapting to "bad" prompts even could work

zamfi 3y ago

LLMs will definitely get better -- and people will adapt too. But natural language contains inherent ambiguity, and that's not going to change. A large part of the paper talks about how the challenges of programming and developing ML systems don't go away just because you're using natural language and an LLM. For example, users incorrectly extrapolating from a single failed/successful prompt to other contexts: that's not something that will be solved by better LLMs, really. It's solved by getting users to recognize when they do/don't have enough data to believe what they're seeing. Disclaimer: I wrote the paper.

digdugdirk 3y ago
Thanks for the official TL/DR. ... And the laughs. Paper is great, too. What's it like trying to write a paper while the field is shifting so rapidly? Is there anything you would have liked to include that had to be cut so you could get it out the door?
- zamfi 3y ago
  Oh, that's not a full TL;DR -- but sounds like you've read the paper. :) Shifting field: yes, we spend a lot of time trying to figure out what is going to change and what isn't, at least in HCI (my field). LLM capabilities are changing by the...day? So anything that's very focused on LLM capabilities won't be relevant for long. Humans don't change quite as fast! Keep in mind this paper was first written in September 2022, reflecting work done prior to then...practically an eternity in which we've since seen ChatGPT take the world by storm, and now GPT-4. But I think the lessons there are still very relevant, and will be for quite some time.