Bayesian Fine-tuning Yields Language Models that are as Bayesian as their Beliefs Allow
cs.CL
Submitted: 2026-09-30
Updated: 2026-09-30
Terminology
Sources
- Belief Dynamics Reveal the Dual Nature of In-Context Learning and Activation Steering
- Brittle Minds, Fixable Activations: Understanding Belief Representations in Language Models
- Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
- Inside-Out: Hidden Factual Knowledge in LLMs
- The Llama 3 Herd of Models
- Large Language Models Assume People are More Rational than We Really are
- Reasoning over Uncertain Text by Generative Large Language Models
- Constrained belief updates explain geometric structures in transformer representations
- Reasoning Under Uncertainty: Exploring Probabilistic Reasoning Capabilities of LLMs
- Qwen2.5 Technical Report
- BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Gemma 2: Improving Open Language Models at a Practical Size
- Function Vectors in Large Language Models
- Instructions Shape Production of Language, not Processing
- An Explanation of In-context Learning as Implicit Bayesian Inference
- What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization
- Incoherent Probability Judgments in Large Language Models
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