Temperature is a measure of capriciousness. How likely is the model to choose a token that is not “the most likely” next token.
It’s not a big ask to look this up. But even if you don’t, making a point to show that you don’t know it seems bad.
It’s not a big ask to look this up. But even if you don’t, making a point to show that you don’t know it seems bad.
Yes, that’s what “temperature” means, what does a temperature of 0.7 mean?
> It’s not a big ask to look this up. But even if you don’t, making a point to show that you don’t know it seems bad.
Well, no, making a point of highlighting the points of your ignorance when discussing something is good. Especially when you are a notable expert in the broad field being discussed.
> Well, no, making a point of highlighting the points of your ignorance when discussing something is good. Especially when you are a notable expert in the broad field being discussed.
I disagree. Stating “whatever that means” indicates dismissiveness, not a transparent lack of expertise. Also, you should know what it means if you’re an expert.
This quote implies to me that he is actually a beginner when it comes to this technology but is expecting to be treated like an expert whose experience generalizes
Knuth is a world expert in randomized algorithms. Do you think he doesn't have a good intuition for what could be happening? But he's a stickler for detail, and temperature is an obfuscation.
There is no reason to assume that OpenAI has changed the definition of this term.
> In the OpenAI GPT API, the temperature parameter controls the randomness of the model's output. A temperature value of 0.7 in the GPT API means that the model's responses will have a moderate level of randomness.
> When generating responses, a higher temperature value, such as 1.0, makes the output more random and creative. This can result in more diverse and unpredictable responses. On the other hand, a lower temperature value, such as 0.2, makes the output more focused and deterministic, with the model tending to choose more probable and conservative responses.
> By setting the temperature to 0.7, you can expect the model's responses to strike a balance between randomness and coherence, offering a mix of expected and unexpected answers.
> At a deeper level, the temperature parameter in generative AI models like GPT-3 affects the softmax function, which is used to convert the model's output into a probability distribution over the possible words or tokens.
> The softmax function takes the logits, which are the unnormalized output values of the model, and applies the exponential function to each value, normalizing them to produce probabilities. The formula for the softmax function is as follows:
> P(y_i) = exp(logit(y_i)) / sum(exp(logit(y_j)))
> Where:
> P(y_i) is the probability of the i-th token in the output sequence. logit(y_i) is the logit value (unnormalized score) of the i-th token. > The sum is taken over all possible tokens. > The temperature parameter comes into play when computing the exponential function. The temperature value, denoted as T, is applied as an exponent to the logits before performing the softmax. Mathematically, the modified softmax function with temperature is:
> P(y_i) = exp(logit(y_i) / T) / sum(exp(logit(y_j) / T))
> The temperature parameter T controls the steepness of the softmax function. A higher temperature value (e.g., 1.0) increases the exponentiation effect, making the probabilities more spread out and the distribution more uniform. This leads to a higher likelihood of selecting less probable tokens, resulting in more diverse and random output.
> Conversely, a lower temperature value (e.g., 0.5) reduces the exponentiation effect, making the probabilities sharper and more focused. The most probable tokens are emphasized, leading to more deterministic and conservative output.
> By adjusting the temperature, you can manipulate the balance between exploration and exploitation during text generation. Higher temperatures encourage the model to explore more possibilities, while lower temperatures bias it towards exploiting the most likely choices based on the training data.
> It's important to note that the choice of temperature is a design decision, and there is no fixed rule for determining the optimal value. It often requires experimentation and understanding the specific behavior of the model to achieve the desired output characteristics
For the guy who doesn't even use email? https://www-cs-faculty.stanford.edu/~knuth/email.html
I do not feel it is appropriate for you to say you have looked it up if you don’t know what it is besides an API input that affects randomness.