Quick Takeaways
- Language models tend to favor specific numbers like 47, 37, or 73, making their outputs more predictable than a fair random generator.
- Post-training tuning narrows the range of likely answers, reducing unpredictability, but prompt wording and format can still significantly influence the results.
- Traditional security measures like entropy and expected guesses reveal that these models’ answers are surprisingly guessable, risking secret protection if used as random sources.
- To improve security, it’s recommended to rely on true random generators and have models produce code or instructions to access them, rather than generating random numbers directly.
Understanding How AI Picks Random Numbers
Many people wonder how “random” an AI’s answer really is. When you ask a chatbot to pick a number between 1 and 100, it often favors certain numbers. For example, some models pick 47 or 73 more often than others. This is because models inherit biases from their training data. Unlike a fair coin or a digital random generator, AI responses are not perfectly even. Researchers analyze the probability of each number from the model’s answer probabilities. They find that some numbers appear surprisingly often. This means guessing the AI’s favorite number takes fewer tries than guessing randomly. So, even though the AI seems to pick randomly, it favors specific answers, changing how secure or unpredictable that number really is.
Measuring How Guessable the Numbers Are
To see how predictable an AI’s pick is, experts use three main measures: Shannon entropy, min-entropy, and guessing entropy. Shannon entropy looks at how surprising each answer is, on average. For a fair number, it’s about 6.64 bits, meaning many guesses are needed. Min-entropy looks only at the favorite answer, measuring how easy it would be for a guesser to succeed with just one try. If the AI favors one number heavily, it requires fewer guesses. Guessing entropy predicts the average number of tries to crack the answer, based on sorting answers by probability. If one number is heavily favored, all three measures show the pick is easier to guess than fair. These tools help understand risks, especially if someone wants to keep a secret safe from guessing.
Why the Bias Matters and How to Improve It
Models trained on human text tend to favor certain numbers naturally. Instruction tuning can make the AI’s answers less predictable, but results vary. When models are fine-tuned or re-trained, their favorite answers often change. However, prompt wording and format also influence results. Rephrasing a question can shift the probabilities, making the number more or less guessable. Relying on the AI to generate a random number is risky, because it might favor specific answers. The best approach is to use dedicated random number generators, like software tools, instead of AI. These tools produce truly unpredictable outcomes. Language models are great for deciding when and where to get a number, but the actual sourcing should come from secure, dedicated randomness sources to protect sensitive information.
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