Unverified50% confidenceFactExact time
When an LLM generates text, it first computes a probability distribution over the entire vocabulary (usually tens of thousands to over a hundred thousand tokens) for each token, and then samples the next token according to the probabilities.
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10/13/2026
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UnverifiedLLMs are essentially next-token prediction machines based on statistical patterns, and their output is the text sequence with the highest probability given the context.78% similarUnverified对于大语言模型推理，吞吐通常以 token 数为单位衡量，即 tokens/s 是评估 LLM 推理框架性能的核心指标74% similarUnverified当前 LLM 在代码生成时是基于概率分布生成下一个 token，而非基于工程经验做出系统性设计决策72% similarUnverifiedThe LLM-as-judge evaluation approach requires consuming tokens to generate the answer to be evaluated, and then consuming additional tokens to have another model score it, resulting in double or even multiple times the token overhead70% similarVerifiedLLMs generate output token by token in an autoregressive manner, with each word's selection based on probability sampling from the current context.68% similar
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