Decoding Strategies in Large Language Models

Decoding strategies play a crucial role in the text generation process of Large Language Models (LLMs) like GPT-2, complementing the focus on model architectures and data processing. This article explores the mechanics of different decoding methods, including greedy search, beam search, top-k sampling, and nucleus sampling. Greedy search quickly selects the most probable token at each step, but can miss more optimal sequences. Beam search considers multiple potential sequences, leading to more nuanced results, while top-k sampling introduces randomness by selecting from a set of the most likely tokens, and nucleus sampling dynamically chooses tokens based on cumulative probability. Each method offers unique strengths, with the choice depending on the desired balance between predictability and creativity in the generated text. Through illustrations and code examples, the article provides insights into tuning parameters like temperature and num_beams to guide LLMs toward producing diverse and coherent outputs.