Understanding LLM Sampling: Top-K, Top-P, and Temperature

Author(s): Sai Bhargav Rallapalli

Originally published on Towards AI.

Mastering Creativity and Control with Temperature, Top-K, and Top-P

LLM sampling is how a model decides the next word to generate from a list of possibilities. Rather than simply picking the most likely word, sampling techniques like Top-K, Top-P, and Temperature introduce controlled randomness to create more natural and creative text.

The article discusses the fundamental techniques of LLM sampling, namely Top-K, Top-P, and Temperature, outlining how they influence text generation. Top-K confines choices to a fixed number of likely words, while Top-P adapts by selecting words based on cumulative probability thresholds. Temperature, on the other hand, adjusts the entire probability distribution of outcomes to control the randomness of the generation. Each method has its pros and cons, indicating a balance between predictability and creativity, ultimately guiding users in customizing LLM outputs effectively.

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Published via Towards AI