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    UChicago professor flags language model probability mismatch

    Assistant professor at UChicago notes that model outputs for rain and no rain may fail to sum to one.

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    1 Source, 27d ago, first seen 27d ago

    TLDR

    Suproteem Sarkar, Assistant Professor at UChicago, posted an example in which a language model is asked the probability of rain tomorrow and then the probability it will not rain. The post states that the two probabilities should sum to one regardless of the outcome yet shows a case where the model generates 0.7 for rain and 0.2 for no rain. The tweet links to a flowchart diagram titled "Can you arbitrage a language model? Evaluating probabilistic coherence of model forecasts" that depicts two parallel rows of four connected boxes.

    Combined views

    65K

    1 Source, first seen 27d ago

    Combined views

    65K

    1 Source, first seen 27d ago

    381 likes
    381 likes
    18 comments
    311 saves
    34 reposts

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    18 comments
    311 saves
    34 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    1 Source

    @SuproteemSarkarsuppose you ask a language model the probability it will rain tomorrow. then you ask the probability it will not rain tomorrow whatever happens tomorrow, those two probabilities should sum to one but suppose the model generates P(rain) = 0.7 and P(no rain) = 0.2

    1 Source

    @SuproteemSarkarsuppose you ask a language model the probability it will rain tomorrow. then you ask the probability it will not rain tomorrow whatever happens tomorrow, those two probabilities should sum to one but suppose the model generates P(rain) = 0.7 and P(no rain) = 0.2