Controlling LLM Output Variation: An Empirical Look at Temperature, Top-p, Top-k, and Repetition Penalty
Published:Jan 9, 2026 16:34
•1 min read
•Zenn LLM
Analysis
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.
Key Takeaways
- •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.
Reference
“本記事のコードは、Temperature / Top-p / Top-k の挙動差を API なしで体感する最小実験です。”