Categories: AI/ML Research

Decoding Strategies and Output Control

This chapter is divided into nine parts; they are: • Reading Logits from a Model • Greedy Decoding • Temperature Sampling • Top-$k$ Sampling • Nucleus Sampling • Repetition Penalties • Beam Search • Stop Conditions • Structured Output Constraints The model returns a vector of logits for every position in the input sequence.

How LLMs Choose Their Words: A Practical Walk-Through of Logits, Softmax and Sampling

This article is divided into four parts; they are: • How Logits Become Probabilities • Temperature • Top- k Sampling • Top- p Sampling When you ask an LLM a question, it outputs a vector of logits.

December 16, 2025

In "AI/ML Research"

Understanding Text Generation Parameters in Transformers

This post is divided into seven parts; they are: - Core Text Generation Parameters - Experimenting with Temperature - Top-K and Top-P Sampling - Controlling Repetition - Greedy Decoding and Sampling - Parameters for Specific Applications - Beam Search and Multiple Sequences Generation Let's pick the GPT-2 model as an…

April 22, 2025

In "AI/ML Research"

The Statistics of Token Selection: Logits, Temperature, and Top-P Walkthrough

When large language models, or LLMs for short, produce outputs, several criteria are at stake, including not only overall response relevance but also coherence and creativity.

May 28, 2026

In "AI/ML Research"

AI Generated Robotic Content