Verified65% confidenceFactExact time
During training, LLMs optimize parameters by maximizing the prediction probability of the next token, meaning the model learns the statistical distribution of the training corpus rather than human aesthetic judgment.
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65%
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Long-term
Relevance
7/16/2026
First Seen
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Related Claims
UnverifiedThe core mechanism of an LLM is to predict the next most likely token — that is, to find the highest-probability path in the training data distribution.81% similarUnverifiedLLM-Blender 通过训练专用排序模型 PairRanker 来评估多个 LLM 的候选输出并融合最优结果79% similarVerifiedLLMs are fundamentally probability distribution systems trained on massive amounts of text, where each output is a sampling of the next most likely token rather than retrieval and reasoning based on real-world knowledge.77% similarUnverifiedIf the proportion of padding tokens in the training data is too high, the model will tend to predict this high-frequency symbol.77% similar
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