Surprisal minimization over goal-directed alternatives generated by language models provides the strongest account of production choices in open-ended dialogue compared to uniform information density or length-based costs.
Surprise! U niform I nformation D ensity Isn ' t the Whole Story: Predicting Surprisal Contours in Long-form Discourse
2 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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Globally salient entities exhibit higher surprisal and reduce surprisal in surrounding text, refining the UID hypothesis by adding entity salience as a shaping factor.
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Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue
Surprisal minimization over goal-directed alternatives generated by language models provides the strongest account of production choices in open-ended dialogue compared to uniform information density or length-based costs.
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Expect the Unexpected? Testing the Surprisal of Salient Entities
Globally salient entities exhibit higher surprisal and reduce surprisal in surrounding text, refining the UID hypothesis by adding entity salience as a shaping factor.