Heads, Tails, and AI Fails: LLMs, Randomness, and Human Judgments

arXiv:2406.00092v2 Announce Type: replace Abstract: Randomness is central to human cognition and to many applications in which large language models are deployed, yet probabilistic token generation does not imply that LLMs can produce unbiased random sequences. We study how contemporary LLMs generate binary random sequences using the classic behavioral-science paradigm of simulated coin flips. Across single flips, 20-flip sequences, n-gram statistics, run lengths, alternation rates, and next-flip predictability, we compare model outputs to both true Bernoulli baselines and human data from prio

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