This article walks through the mechanics of greedy search and beam search in large language models, demonstrates both methods with GPT‑2 on the prompt "I have a dream", visualizes the decoding trees, compares their scores, and discusses the trade‑offs between efficiency and output quality.
The article explains how sampling parameters—Temperature, Top‑k, and Top‑p—shape the output of large language models, comparing greedy and beam search, illustrating probability changes with concrete examples, and offering practical guidance on adjusting these settings for different tasks.
This review of the recent MindSpore NLP public class walks through the fundamentals of large language model text generation, detailing deterministic decoding such as greedy and beam search, stochastic sampling techniques like temperature, top‑k and top‑p, and advanced methods including constrained beam, contrastive, and assisted search, with illustrative examples.
This tutorial explores various text generation decoding methods—including greedy search, beam search, top‑k/top‑p sampling, sample‑and‑rank, and group beam search—explaining their principles, providing detailed Python code examples, and comparing their use in modern large language models.
