The answer
The prediction depends on the broader available context, including earlier tokens and instructions. Different contexts can give the same word different roles and meanings.
How does an LLM predict the next token in a sequence?
Why can the same final word lead to different next-token predictions?
More FAQs in How does an LLM predict the next token in a sequence?
Not necessarily. Tokens can be shorter than words or include punctuation and other text units. The tokenizer determines which units the model predicts.
A logit is an unnormalized score produced by the model. A normalization operation such as softmax converts the set of scores into probabilities.
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