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Do Language Models Have Bayesian Brains? Distinguishing Stochastic and Deterministic Decision Patterns within Large Language Models
- Language models are essentially probability distributions over token sequences.
- Auto-regressive models generate sentences by iteratively computing and sampling from the distribution of the next token.
- Prior research has assumed that language models make probabilistic decisions similar to sampling from unknown distributions.
- A study questions whether language models exhibit Bayesian decision-making.
- Findings reveal that language models can display near-deterministic decision-making under specific conditions.
- This challenges the assumption of stochastic decision-making and impacts methods for inferring language model priors.
- Systems with deterministic behavior undergoing simulated Gibbs sampling may converge to a false prior without proper scrutiny.
- A proposed approach aims to differentiate between stochastic and deterministic decision patterns in Gibbs sampling.
- The study experiments with various large language models to analyze their decision patterns under different scenarios.
- The research offers valuable insights into understanding the decision-making processes of large language models.
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