Definition
In Generative AI, frequency penalty is setting that prevents a model from reusing a word that has already been used multiple times in its output. Simply put, It is just like telling a model, “You’ve used that word several times, try something new.” The higher the penalty, the less repetitions in the generated text [1].
This parameter
ranges from -2.0 to 2.0, with positive values reducing the chance of repeating
the same words while negative values increases it. If a value is not provided
it will default to 0 — this means no penalty is applied.
For example, an online fashion store can use the frequency penalty to create compelling product descriptions for ankara dresses. When the model starts generating text, a higher frequency penalty (e.g., 0.8) ensures that it avoids repeating the same words and phrases excessively. For instance, instead of generating "This ankara dress is beautiful. This ankara dress is colorful. This ankara dress is perfect for parties," the model might produce "This ankara dress is beautiful. Its vibrant colors make it ideal for owambe celebrations and special occasions." This results in a more diverse and engaging descriptions that convince customers to buy.
Setting the frequency penalty in OpenAI’s GPT-4o [2].
Origin
Frequency
Penalty emerged in the Transformer era (post-2017). As models grew larger and
generated longer paragraphs, engineers came up with the concept as a solution to
force a model to be more creative and less "robotic."
Context and
Usage
Some of the use cases
of
frequency penalty include the following:
- Chatbots: Delivers diverse, genuine responses, that makes conversations more interesting.
- Content Creation: Produces enticing, coherent text for blogs, articles, and more.
- Story Generation: Improves storytelling by creating diverse and more engaging narratives [3].
Why it Matters
Language models
(LMs) have the problem of repetition when generating text. Without supervision,
models tend to reuse words, phrases, or concepts repeatedly, particularly in
long-form outputs. This leads to low quality generated output.
To solve this
problem, most LLMs provide two complementary features: frequency penalty and
presence penalty. Both of these parameters adjust the probabilities of tokens
during sampling, encouraging the model to generate more diverse, creative, and
contextually rich outputs [4].
Related Model Training and Evaluation Concepts
- Gradient Descent: Optimization algorithm that iteratively adjusts model parameters to minimize loss.
- Hyperparameter: Configuration setting defined before training that controls the learning process.
- Hyperparameter Tuning: Process of finding optimal hyperparameter values to improve model performance.
- Inference: Process of using a trained model to make predictions or generate outputs on new data.
- Instruction Tuning: Training method that teaches models to follow specific instructions and commands.
In Practice
OpenAI’s ChatGPT
is a good real-life case study of frequency penalty in practice. Although ChatGPT’s
chat interface do not offer parameter tuning, this functionality is generally
available when you interact with its application programming interface (API). With
OpenAI’s API and its Python library, you can configure their parameters
effectively, influencing the model's output [5].
References
- VellumAI. (n.d). What is Frequency Penalty and how to use it.
- Heinig, I. (2025). How to improve AI agents performance with these 5 LLM settings.
- Mustafa, J. (2025). Fundamentals of AI: Frequency Penalty.
- PawaAI. (n.d). Frequency and Presence Penalties.
- Bhatt, B., Kuka, V. (2025). LLM Parameters Explained: A Practical Guide with Examples for OpenAI API in Python.