How Large Language Models Generate Text: Greedy Search, Beam Search, Top-K, Top-P, and Temperature

2026-06-27 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

Large Language Models generate text token by token, predicting the next token's probability and selecting it via a decoding strategy. The article details six key strategies: Greedy Search, which selects the highest probability token but can lead to repetitive outputs; Beam Search, which maintains multiple candidate sequences for improved overall quality in tasks like translation but is computationally intensive. Sampling methods introduce randomness for more natural text, including Top-K Sampling, which considers the K most probable tokens, and Top-P (Nucleus) Sampling, which dynamically selects tokens whose cumulative probability exceeds a threshold. Temperature further controls randomness, with lower values yielding focused, deterministic output and higher values promoting creativity. Modern chat-based LLMs often combine Top-P, Temperature, and sometimes Top-K for diverse, fluent responses.

Key takeaway

For Machine Learning Engineers optimizing LLM deployments, understanding decoding strategies is crucial for fine-tuning model output. You should experiment with Top-P Sampling and Temperature settings to balance response creativity and factual accuracy, using lower temperatures for deterministic tasks like coding and higher values for creative content generation. Avoid Beam Search for open-ended conversational AI, as it often yields less natural language.

Key insights

LLMs generate text by predicting next token probabilities and applying diverse decoding strategies to balance coherence and creativity.

Principles

• Autoregressive models predict tokens based on prior context.
• Local optimum choices don't guarantee global best sequences.
• Introducing randomness enhances text diversity and naturalness.

Method

An LLM computes a next-token probability distribution from prior text, then a decoding algorithm (Greedy, Beam, Top-K, Top-P, Temperature) selects the subsequent token. This process repeats until completion.

In practice

• Use low Temperature for factual Q&A or coding.
• Employ Top-P Sampling for dynamic candidate token selection.
• Combine Top-P and Temperature for natural chatbot responses.

Topics

• Large Language Models
• Text Generation
• Decoding Strategies
• Top-P Sampling
• Temperature Parameter
• Autoregressive Models

Best for: AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.