Unverified50% confidenceFactExact time
When large language models generate text, the optimization target is the probability of the next token, not the reader's comprehension efficiency.
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50%
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Long-term
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10/24/2026
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UnverifiedLarge language models autoregressively predict the next highest-probability token, causing AI-generated text to have low perplexity.78% similarVerifiedWhen generating text, language models default to selecting word sequences with higher conditional probability.78% similarUnverifiedCurrent mainstream large language models are essentially performing text prediction, calculating the most likely next word based on existing context, with no understanding, intent, or consciousness.78% similarUnverifiedText generation in large language models is autoregressive, and the number of output tokens is nearly proportional to response time75% similarUnverifiedByte-level modeling eliminates the linguistic imbalance introduced by tokenization, but at the cost of significantly increased sequence length, placing greater demands on the efficiency of the attention mechanism.74% similar
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