Bayesian Mixture-of-Experts: Towards Making LLMs Know What They Don't Know

• URL: http://arxiv.org/abs/2509.23830v1
• Date: Sun, 28 Sep 2025 12:07:35 GMT
• Title: Bayesian Mixture-of-Experts: Towards Making LLMs Know What They Don't Know
• Abstract summary: Mixture-of-Experts (MoE) has enabled the creation of massive yet efficient Large Language Models.<n>Standard deterministic routing mechanism presents a significant limitation.<n>This thesis proposes a structured textbfBayesian MoE routing framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The Mixture-of-Experts (MoE) architecture has enabled the creation of massive yet efficient Large Language Models (LLMs). However, the standard deterministic routing mechanism presents a significant limitation: its inherent brittleness is a key contributor to model miscalibration and overconfidence, resulting in systems that often do not know what they don't know. This thesis confronts this challenge by proposing a structured \textbf{Bayesian MoE routing framework}. Instead of forcing a single, deterministic expert selection, our approach models a probability distribution over the routing decision itself. We systematically investigate three families of methods that introduce this principled uncertainty at different stages of the routing pipeline: in the \textbf{weight-space}, the \textbf{logit-space}, and the final \textbf{selection-space}. Through a series of controlled experiments on a 3-billion parameter MoE model, we demonstrate that this framework significantly improves routing stability, in-distribution calibration, and out-of-distribution (OoD) detection. The results show that by targeting this core architectural component, we can create a more reliable internal uncertainty signal. This work provides a practical and computationally tractable pathway towards building more robust and self-aware LLMs, taking a crucial step towards making them know what they don't know.

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