7 ms·

Have you tried replicating via the API with a temp of 0?

belter 3y ago
No I did not.
moffkalast 3y ago
Using a temp of zero usually returns garbage results from most models, so it would likely do so in case of GPT 4 as well. Any other great ideas?
- circuit10 3y ago
  What do you mean? It works fine for me when I’ve tried it
- afro88 3y ago
  Temp of 0 gives the least random and most predictable results
  - moffkalast 3y ago
    That's true, but those results are rarely the correct ones, at least for v1 llama models. In my experience each model has an optimal temperature at which it performs vastly better. I'm sure OpenAI have the best config they know set up for ChatGPT but let people generate trash through the API if they want to waste their credits on it.
    - dontreact 3y ago
      Why would the accuracy decrease with lower temperature? Setting temperature to 0 just means at each step the model will emit the token with the highest likelihood.
      - moffkalast 3y ago
        Yes that's what I'm saying, to reiterate: The likeliest token does not lead to the highest performing result. Otherwise temperature wouldn't even be an option. I would imagine things like language word frequency affect the token rating a lot while having nothing to do with the task at hand except providing a correctly formatted answer, but it's probably not the whole story. OpenAI (and others that know what they're doing) always do their benchmarks in a multi-sampled way, by running 5 or 20 times at optimal temp. Using a wrapper that runs these samples and then another pass that judges self-consistency for a final answer can give you a correct answer 100% of the time for a question that would be wrong 100% of the time with temp at zero.
      - lostmsu 3y ago
        I had a conversation with a friend regarding this exact question and my understanding is that model trains to optimize the distribution of all texts, therefore when you restrict it to deterministic sampling that is not representative of inputs you select the slice of the distribution that model learned that conveys much less information than the full distribution, and hence has poorer results.
- taberiand 3y ago
  The point isn't that temp 0 should be used, the point is that anyone surprised that they get different results should realise that there is an element of randomness involved by default. Even repeating the same question in a single chat can have GPT-4 vary on its output, though it will often settle on a particular output due to context informing the output (which is why adding context is so important for these models)
- Grimblewald 3y ago
  Not in my experience, in fact I find that when I need precise, realistic, and reliable results temp 0 is needed. For example, here is a bunch of names, gather the names of specific plastics under headings matching their common acronym - if I don't use temp 0 I might get nonsense out. Temp 0? reliably correct.
  - moffkalast 3y ago
    Interesting, that's the exact opposite of my experience.

No I did not.

Using a temp of zero usually returns garbage results from most models, so it would likely do so in case of GPT 4 as well. Any other great ideas?

circuit10 3y ago
What do you mean? It works fine for me when I’ve tried it
afro88 3y ago
Temp of 0 gives the least random and most predictable results
- moffkalast 3y ago
  That's true, but those results are rarely the correct ones, at least for v1 llama models. In my experience each model has an optimal temperature at which it performs vastly better. I'm sure OpenAI have the best config they know set up for ChatGPT but let people generate trash through the API if they want to waste their credits on it.
  - dontreact 3y ago
    Why would the accuracy decrease with lower temperature? Setting temperature to 0 just means at each step the model will emit the token with the highest likelihood.
    - moffkalast 3y ago
      Yes that's what I'm saying, to reiterate: The likeliest token does not lead to the highest performing result. Otherwise temperature wouldn't even be an option. I would imagine things like language word frequency affect the token rating a lot while having nothing to do with the task at hand except providing a correctly formatted answer, but it's probably not the whole story. OpenAI (and others that know what they're doing) always do their benchmarks in a multi-sampled way, by running 5 or 20 times at optimal temp. Using a wrapper that runs these samples and then another pass that judges self-consistency for a final answer can give you a correct answer 100% of the time for a question that would be wrong 100% of the time with temp at zero.
    - lostmsu 3y ago
      I had a conversation with a friend regarding this exact question and my understanding is that model trains to optimize the distribution of all texts, therefore when you restrict it to deterministic sampling that is not representative of inputs you select the slice of the distribution that model learned that conveys much less information than the full distribution, and hence has poorer results.
taberiand 3y ago
The point isn't that temp 0 should be used, the point is that anyone surprised that they get different results should realise that there is an element of randomness involved by default. Even repeating the same question in a single chat can have GPT-4 vary on its output, though it will often settle on a particular output due to context informing the output (which is why adding context is so important for these models)
Grimblewald 3y ago
Not in my experience, in fact I find that when I need precise, realistic, and reliable results temp 0 is needed. For example, here is a bunch of names, gather the names of specific plastics under headings matching their common acronym - if I don't use temp 0 I might get nonsense out. Temp 0? reliably correct.
- moffkalast 3y ago
  Interesting, that's the exact opposite of my experience.