Greedy search directly picks the highest-probability word at each step and easily falls into local optima; beam search keeps several candidate sequences at once and is commonly used in tasks with high accuracy requirements such as machine translation, but it appears stiff in open-ended dialogue; chat applications mostly use sampling with temperature and Top-p, whereas tasks such as summarization and translation more often use beam search.