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.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , month =
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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.
Lexical frequency is a stronger predictor of metaphor novelty than LM surprisal, with the surprisal-novelty link peaking early in training before declining as surprisal becomes more aligned with frequency.
citing papers explorer
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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.
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The Frequency Confound in Language-Model Surprisal and Metaphor Novelty
Lexical frequency is a stronger predictor of metaphor novelty than LM surprisal, with the surprisal-novelty link peaking early in training before declining as surprisal becomes more aligned with frequency.