Nucleus Sampling

Nucleus sampling is a text generation technique used in large language models (LLMs) to control the randomness and diversity of generated text. Current research focuses on improving its effectiveness, addressing issues like memorization of training data and generating repetitive or incoherent outputs, and exploring alternative sampling methods such as priority sampling and conformal nucleus sampling to enhance control and provide uncertainty quantification. These advancements are crucial for improving the reliability and trustworthiness of LLMs in various applications, including code generation, machine translation, and medical image analysis where accurate and diverse outputs are essential.