Unverified50% confidenceSolutionExact time
The LLM's symptom extraction process is based on a standard vocabulary written by clinicians, ensuring structured and predictable output.
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50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
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VerifiedLLMs generate output token by token in an autoregressive manner, with each word's selection based on probability sampling from the current context.68% similarUnverifiedLLMs generate text in an autoregressive manner, predicting the next token one at a time, with each prediction based on all prior context.67% similarUnverifiedLLMs are essentially next-token prediction machines based on statistical patterns, and their output is the text sequence with the highest probability given the context.66% similarUnverifiedLarge language models have absorbed vast amounts of technical documentation and troubleshooting guides during training, forming a 'symptom–hypothesis–verification' reasoning pattern that can dynamically adjust diagnostic paths based on context.65% similarUnverifiedLLM可通过分析输出token的对数概率分布、思维链末尾自我评估、温度采样统计答案分布熵值等手段近似估计决策置信度65% similar
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