Controlling LLM Output Variation: An Empirical Look at Temperature, Top-p, Top-k, and Repetition Penalty

This article provides a hands-on exploration of key LLM output parameters, focusing on their impact on text generation variability. By using a minimal experimental setup without relying on external APIs, it offers a practical understanding of these parameters for developers. The limitation of not assessing model quality is a reasonable constraint given the article's defined scope.

• •The article demonstrates the behavioral differences of Temperature, Top-p, and Top-k sampling strategies.
• •It utilizes a minimal experimental setup based on Python and NumPy.
• •The focus is on understanding parameter effects, not evaluating overall model performance.