Language Models and Time: Why ChatGPT Gets Deadlines Wrong
LLMs generate answers instantly, yet they often talk about deadlines as if they were real. In fact, language models have no concept of time: they simply repeat patterns from their training data. This is the first article in a series on how humans and AI think together.
AI-processed from Habr AI; edited by Hamidun News
Language models like ChatGPT generate an answer in two seconds, yet confidently say: "This task will take two weeks." Something more fundamental lies behind this contradiction: they have nothing resembling what we call time.
Why does a neural network lie about time and deadlines?
Language models work token by token, predicting the next word with no internal clock. For them, time is just words from the training data, not a continuum of past and future that they actually experience.
Why do language models misjudge how long tasks take?
Because language models work token by token, predicting the next word in a sequence. They have no internal clock, and for them time is just words from the training data, not a real understanding of time intervals.
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Key Questions Answered
Why does a neural network lie about time and deadlines?
Language models work token by token, predicting the next word with no internal clock. For them, time is just words from the training data, not a continuum of past and future that they actually experience.
Why do language models misjudge how long tasks take?
Because language models work token by token, predicting the next word in a sequence. They have no internal clock, and for them time is just words from the training data, not a real understanding of time intervals.
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