Understanding LLM Temperature: How It Controls Token Selection
_jaydeepkarale · x · 2026-08-13
This thread explains how the temperature parameter works in LLM APIs. When predicting the next token, the model generates a probability distribution (e.g., 92% for Paris as France's capital). temperature modifies this distribution before token selection: low temperatures sharpen it, allowing high-probability tokens to dominate for predictable outputs; high temperatures flatten it, increasing the chances of selecting lower-probability tokens and adding variability.
More from Research
- New Eval Benchmark Tailored for AI in Academic Peer Review — ChenhaoTan · 2026-08-13
- Mechanism Interferometry: A Causal Calculus to Verify Neural Network Modularity — doodlestein · 2026-08-13
- micro1 Executive Shares Key Insights on AI World Models — Exp_Mark · 2026-08-13
- LinkedIn's Self-Evolving Support Agent Boosts Routing Accuracy by 30%+ — davemccollough · 2026-08-13
- Google Introduces ResidencyRL: Training AI Doctors via Simulated Clinical Practice — SRSchmidgall · 2026-08-13
- AI for Science: Designing mRNA Sequences with Evo 2 and Other Models — BrianHie · 2026-08-13