Bayesian Teaching for LLMs
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Google researchers introduce a method to teach LLMs to reason like Bayesians by fine-tuning on interactions with a Bayesian Assistant that represents optimal probabilistic inference. LLMs normally fall far short of normative Bayesian reasoning, but this training approach dramatically improves their ability to update predictions based on new evidence.
Bayesian Assistant as teacher: The method constructs synthetic training data from interactions between users and an idealized Bayesian Assistant. By exposing the LLM to examples of optimal belief updating, the model learns to approximate Bayesian inference without any architectural changes.
Generalization to new tasks: The trained models do not just memorize the training distributions. They generalize probabilistic reasoning to entirely new task types, suggesting that Bayesian inference can be instilled as a transferable capability through carefully designed fine-tuning data.
Closing the gap with normative models: Before training, LLMs show systematic deviations from Bayesian predictions, including base rate neglect and conservatism. After Bayesian teaching, these biases are substantially reduced, bringing model predictions much closer to the normative standard.
Data quality over model scale: The results reinforce a recurring theme in recent research: carefully curated training data can unlock capabilities that scale alone cannot. A smaller model trained on Bayesian interactions outperforms larger models reasoning from scratch.
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