How to generate text: using different decoding methods for language generation with Transformers

The article discusses various decoding methods for text generation using Transformer models, including greedy search, beam search, top-k sampling, and nucleus sampling (top-p). It provides comparative analyses of these techniques in terms of output quality and diversity, highlighting that while greedy search is fast, it often produces repetitive outputs, whereas top-k and nucleus sampling yield more varied results at the cost of increased computational complexity. Understanding these decoding strategies is crucial for practitioners to optimize text generation tasks according to specific application requirements, balancing quality and efficiency.