ראשים, כפופים וכישלונות AI: LLMs, אקראיות והחלטות אנושיות

תקציר מקורי באנגליתarXiv:2406.00092v2 Announce Type: replace-cross 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 prior work. We find that LLMs reproduce several canonical human randomness biases, including over-alternation, aversion to long runs, and first-flip biases, but often amplify them or introduce model