Decoding Strategies in Large Language Models
In the fascinating world of large language models (LLMs), much attention is given to model architectures, data processing, and optimization. However, decoding strategies like beam search, which play a crucial role in text generation, are often overlooked. In this article, we will explore how LLMs generate text by delving into the mechanics of greedy search and beam search, as well as sampling techniques with top-k and nucleus sampling.
By the conclusion of this article, you’ll not only understand these decoding strategies thoroughly but also be familiar with how to handle important hyperparameters like temperature, num_beams, top_k, and top_p.
To kick things off, let’s start with an example. We’ll feed the text “I have a dream” to a GPT-2 model and ask it to generate the next five tokens (words or subwords).
The sentence “I have a dream of being a doctor” appears to have been generated by GPT-2. However, GPT-2 didn’t exactly produce this sentence.
