Sampling methods, such as top k and nuclear sampling, generate fluent outputs from language models, but contrastive search offers superior results. Developed by a consortium of researchers, contrastive search significantly enhances the human-like quality of generated text. Those deploying LLMs should prioritize this innovative approach for optimal performance.
