Understanding the Similarities in Responses of Large Language Models to This “Random” Number Question

Large language model (LLM) chatbots, such as ChatGPT, ClaudeAI, and Bing’s Co-Pilot, exhibit a peculiar bias when asked to generate random numbers, often selecting 27 when guessing a number between 1 and 50. This consistent output has caught the attention of curious users and researchers alike. Testing by IFLScience revealed that while LLMs have their reasoning processes, they tend to favor specific numbers, like primes, due to their probabilistic nature. Studies show that humans also have biases in number selection, often leaning toward numbers containing 7. Interestingly, while LLMs aim for unpredictability, their choices remain surprisingly predictable. The phenomenon might stem from training data and reinforcement learning from human feedback, leading to a form of “mode collapse” in number selection. This behavior highlights the challenges LLMs face in generating truly random numbers, prompting further exploration into their underlying mechanisms.

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