Unverified50% confidenceFactExact time
Traditional language models use a token-by-token generation approach, where only one token can be output per run, which is then added to the context before running again.
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9/11/2026
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UnverifiedModern large language models generate code in an auto-regressive manner, predicting the next token each time by referencing only the existing context.76% similarUnverified大语言模型的输出token通常比输入token贵2-3倍，因为生成过程是自回归的，每次只生成一个token并重新运行前向传播75% similarUnverifiedThe ReAct pattern interweaves the language model's reasoning (Chain-of-Thought) with action calls to external tools within the same loop74% similarUnverifiedLarge language models predict the next token one at a time through an autoregressive generation mechanism, and are naturally inclined to generate more complete rather than more concise code.73% similarUnverifiedLarge language models generate text in an autoregressive manner, where the production of each token depends on the context formed by all preceding tokens, following the chain rule of probability.73% similar
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