AI Struggles to Pick a Random City: Language Models Show Surprising Biases
In brief
- AI language models, while advanced, have a surprising flaw: they struggle to generate truly random outputs.
- For instance, when asked to name a random weekday, Qwen3 chooses Wednesday 80% of the time.
- Similarly, Gemma-3 cites just four cities in response to city requests, and multiple-choice questions often place the correct answer as option C.
- This bias isn't just an amusing quirk-it has serious implications for tasks like synthetic data generation and creative problem-solving, where diversity is crucial.
- Recent studies reveal that these biases stem from how models are trained.
- They lack incentives to spread probability across diverse options, leading them to "collapse" onto narrow modes even when broader diversity is needed.
- found that models' sampling from known distributions is heavily skewed, while Gu et al.
- highlighted the consistent positioning of correct answers in multiple-choice questions.
- Efforts to address this issue are mixed.
- One method involves having models first generate a random string and then manipulate it, which works in simple cases but struggles with complexity.
- Another approach focuses on training models explicitly against known distributions to improve their stochastic behavior.
- Early evaluations show promise in distributional fidelity and transfer, suggesting that better randomness could soon be on the horizon.
Terms in this brief
- Qwen3
- A language model that has shown a tendency to select 'Wednesday' with high frequency when asked for a random weekday.
- Gemma-3
- Another language model identified in the brief, which only cites four cities when asked about city names.
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