Frequency_penalty and presence_penalty are two parameters that can be used when generating text with language models, such as GPT-3.
- Frequency_penalty: This parameter is used to discourage the model from repeating the same words or phrases too frequently within the generated text. It is a value that is added to the log-probability of a token each time it occurs in the generated text. A higher frequency_penalty value will result in the model being more conservative in its use of repeated tokens.
- Presence_penalty: This parameter is used to encourage the model to include a diverse range of tokens in the generated text. It is a value that is subtracted from the log-probability of a token each time it is generated. A higher presence_penalty value will result in the model being more likely to generate tokens that have not yet been included in the generated text.
Both of these parameters can be adjusted to influence the overall quality and diversity of the generated text. The optimal values for these parameters may vary depending on the specific use case and desired output.