Unverified50% confidenceFactExact time
The parameter count of traditional large language models is fixed once training is complete, and the model's knowledge is frozen in the weight matrices
1
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50%
Confidence
Long-term
Relevance
9/17/2026
First Seen
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VerifiedThe knowledge of traditional language models comes entirely from training data, with a hard limitation of a knowledge cutoff date, making it impossible to perceive events that occurred after training was completed.79% similarUnverified大语言模型本身是无状态的，其所有知识编码在训练后固定不变的神经网络权重中，单次推理完成后中间状态归零79% similarUnverifiedLarge language models rely solely on parameter weights fixed during training for autoregressive generation at inference time, and have two inherent limitations: a knowledge cutoff and capability boundaries79% similarVerifiedWhen language models are repeatedly trained on data generated by themselves or similar models, systematic degradation occurs, with the diversity of model outputs decreasing with each generation and the tail information of the distribution gradually being lost.74% similarUnverifiedAfter training is complete, a large model's knowledge is fixed at the training cutoff and it cannot perceive new information after that point; Function Calling allows the model to call external functions to obtain additional capabilities or real-time data.73% similar
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