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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Character distribution patterns differ between humans and AI in domain-specific ways, enabling improved AI text detection via the new LD-Score when combined with existing tools on the MDTA benchmark.
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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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Beyond Perplexity: Character Distribution Signatures and the MDTA Benchmark for AI Text Detection
Character distribution patterns differ between humans and AI in domain-specific ways, enabling improved AI text detection via the new LD-Score when combined with existing tools on the MDTA benchmark.