Unverified50% confidenceFactExact time
An autoregressive language model produces a logit vector of vocabulary size at each prediction step, and its structure determines the conditional probability distribution of the next token.
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10/14/2026
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UnverifiedFor multiple-choice questions, the logits over each option token can be normalized via softmax to obtain a probability distribution, where a highly concentrated distribution indicates the model is more confident in the answer79% similarUnverifiedLarge language models such as ChatGPT work by making probabilistic predictions based on massive text data, essentially predicting the next most likely word rather than truly understanding causal relationships.74% 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.73% similarVerifiedA large model is essentially a probability-based text generator that produces content by predicting the next token.72% similar
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