AI Interview Series #1: Explain Some LLM Text Generation Strategies Used in LLMs

Recent advancements in large language models (LLMs) highlight the importance of decoding strategies in shaping generated text. Techniques such as Greedy Search, Beam Search, Nucleus Sampling, and Temperature Sampling enable LLMs to balance coherence, creativity, and diversity by guiding token selection during response generation, with each method offering different trade-offs in speed and output quality. For instance, Greedy Search selects the highest-probability token at each step, providing fast but often repetitive results, while Beam Search maintains multiple candidate sequences to improve coherence and contextual relevance. These decoding strategies are crucial for optimizing LLM performance across