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Avoidable and Unavoidable Randomness in GPT-4o
- The article explores randomness in GPT-4o through coin flipping prompts and analysis of determinism.
- GPT-4o's coin flips show bias resembling human tendencies observed in previous studies on coin flipping.
- Using token probabilities rather than full responses, a method for precise evaluation of GPT-4o coin flip outcomes is presented.
- Factors like temperature, seed, and system_fingerprint affect randomness in GPT-4o's responses but do not ensure determinism.
- The mixture-of-experts architecture in GPT-4o introduces non-determinism beyond controllable parameters like temperature and seed.
- GPT-3.5-turbo also exhibits non-deterministic log probabilities, indicating sources of randomness beyond mixture-of-experts.
- An experiment with 10,000 coin flips in GPT-4o reveals 42 distinct probabilities, suggesting hidden sources of non-determinism.
- The article highlights the challenge of studying non-deterministic models like GPT-4o and the limitations of controlling randomness in AI responses.
- Transparency and understanding of hidden sources of randomness in AI models remain crucial for researchers to analyze model behavior accurately.
- Mixture-of-experts models introduce randomness due to expert allocation based on batched prompts, contributing to non-determinism in AI outputs.
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