Study finds conversational AI systems display human-like number selection preferences
Researchers analyzing probability distributions across eighteen open-source language models discovered that instruction-tuned models regularly avoid choosing boundary numbers such as 1 or 100 as well as round figures. The study indicates that conversational fine-tuning introduces non-random biases that resemble human psychological tendencies.
