What is Top_P?
An alternative to sampling with temperature is called nucleus sampling, in which the model only takes into account the tokens with the highest probability mass (as determined by the top_p parameter).
For example, a value of 0.1 means that only the tokens with the top 10% probability mass are considered.
OpenAI generally recommends to use either temperature sampling or nucleus sampling, but not both.
You can adjust your top_p value in settings page of our plugin as shown below.
Adjusting the Top_P Setting
When our plugin is installed for the first time, it comes with 0.01 as the default Top_P value.
However you can change it from the Top_P field in the AI Engine tab.
Here are the steps:
- First, navigate to the plugin menu on your WordPress dashboard.
- Click on the Settings page and look for the AI Engine tab.
- Enter a new value in the Top_P field.
- Click on the Save button to save your changes.
Balancing Diversity in GPT Text Generation
The Top_P parameter is a control for the diversity of the generated text produced by GPT.
When generating text with GPT, the model produces a probability distribution over the vocabulary for each word in the generated text.
The Top_P parameter controls how many of the highest-probability words are selected to be included in the generated text.
Specifically, it sets a threshold such that only the words with probabilities greater than or equal to the threshold will be included.
The threshold is calculated by taking the Top P percent of the words with the highest probabilities.
For example, if top_p=0.9, then the threshold is set to the probability of the word that is in the 90th percentile of the words with the highest probabilities.
The value of Top_P can be set between 0 and 1, where a lower value will result in more diverse generated text and a higher value will result in more repetitive or "safe" text.
- Lower value: More diverse generated text.
- Higher value: More repetitive or "safe" text.
When you increase the Top_P the model will tend to produce more conservative text because, with the threshold of probability, most probable outcomes will be considered, thus resulting in less diversity.
Also, with a higher top_p parameter, the model may not take any risks in generating new texts and instead, will tend to select the more common/repetitive words.
On the other hand, with a low Top_P parameter, the model will tend to generate more diverse text and may take risks by selecting less probable words. However, this may also increase the risk of producing nonsensical or nonsensical sentences.
In summary, the Top_P parameter allows to control the level of “risk” the model is willing to take when generating text. With a higher value, the model will tend to generate more repetitive text, and with a lower value, it will tend to generate more diverse text.