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 National Academy of Sciences , volume =
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Develops ACW-based semantic timescale features showing longer autocorrelation windows associate with generic vocabulary and shorter ones with specific words in both human and LLM speech, with the pattern abolished by randomizing word order and timing.
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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The Dynamics of Human and AI-Generated Language: How Semantics Fluctuates across Different Timescales
Develops ACW-based semantic timescale features showing longer autocorrelation windows associate with generic vocabulary and shorter ones with specific words in both human and LLM speech, with the pattern abolished by randomizing word order and timing.