GPT-3 davinci gives different results with the same prompt - text

Question

I am not sure if you have access to GPT-3, particularly DaVinci (the complete-a-sentence tool). You can find the API and info here
I've been trying this tool for the past hour and every time I hit their API using the same prompt (indeed the same input), I received a different response.
Do you happen to encounter the same situation?
If this is expected, do you happen to know the reason behind it?
Here are some examples
Request header (I tried to use the same example they provide)
{
"prompt": "Once upon a time",
"max_tokens": 3,
"temperature": 1,
"top_p": 1,
"n": 1,
"stream": false,
"logprobs": null,
"stop": "\n"
}
Output 1
"choices": [
{
"text": ", this column",
"index": 0,
"logprobs": null,
"finish_reason": "length"
}
]
Output 2
"choices": [
{
"text": ", winter break",
"index": 0,
"logprobs": null,
"finish_reason": "length"
}
]
Output 3
"choices": [
{
"text": ", the traditional",
"index": 0,
"logprobs": null,
"finish_reason": "length"
}
]

Answer 1

I just talked to OpenAI and they said that their response is not deterministic. It's probabilistic so that it can be creative. In order to make it deterministic or reduce the risk of being probabilistic, they suggest adjusting the temperature parameter. By default, it is 1 (i.e. 100% taking risks). If we want to make it completely deterministic, set it to 0.
Another parameter is top_p (default=1) that can be used to set the state of being deterministic. But they don't recommend tweaking both temperature and top_p. Only one of them would do the job.

Answer 2

OpenAI documenation:
https://beta.openai.com/docs/api-reference/completions/create
temperature number Optional Defaults to 1
What sampling temperature to use. Higher values means the model will
take more risks. Try 0.9 for more creative applications, and 0 (argmax
sampling) for ones with a well-defined answer.
We generally recommend altering this or top_p but not both.
top_p number Optional Defaults to 1
An alternative to sampling with temperature, called nucleus sampling,
where the model considers the results of the tokens with top_p
probability mass. So 0.1 means only the tokens comprising the top 10%
probability mass are considered.
We generally recommend altering this or temperature but not both.