Inference
Frequency and Presence Penalty
Two sampling parameters that discourage a model from repeating itself by lowering the scores of tokens it has already produced.
In depth
Two sampling parameters that discourage a model from repeating itself by lowering the scores of tokens it has already produced. Frequency penalty grows with how many times a token has appeared, which reduces verbatim repetition. Presence penalty applies once a token has appeared at all, which nudges the model toward new topics. In OpenAI's Chat Completions API both range from -2.0 to 2.0, and negative values encourage repetition instead.
Primary source: developers.openai.com
Example
In practice, developers reach for Frequency and Presence Penalty when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Frequency and Presence Penalty?
Two sampling parameters that discourage a model from repeating itself by lowering the scores of tokens it has already produced.
Why does Frequency and Presence Penalty matter for AI developers?
Frequency and Presence Penalty sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Frequency and Presence Penalty?
Developers Digest publishes tutorials and videos that cover Inference topics including Frequency and Presence Penalty. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Active Parameters
The part of a model's parameters that is actually used to process each token.
Inference
Constrained Decoding
A generation technique that restricts which tokens a model may pick at each step so the output is guaranteed to match a format such as a regular expression, a grammar, or a JSON Schema.
Inference
Logits
The raw, unnormalized scores a language model's output layer assigns to every token in its vocabulary at each step, before softmax turns them into probabilities.
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