Title: Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: Uncertainty Quantification, Large Language Models, Mixture of Experts, Parameter Efficient Fine Tuning

Abstract: Accurate uncertainty quantification in large language models (LLMs) is essential for providing credible confidence estimates over their outputs. However, fine-tuned LLMs often exhibit overconfidence in uncertain predictions, which stems from their limited ability to generalize with sparse data. Existing parameter efficient fine-tuning (PEFT) uncertainty quantification methods for LLMs focus on post fine-tuning stage, and thus fail to address the core issue: limited specialization of PEFT adapters to accurately capture task-specific input-output relationships. To address these limitations, we propose Functional-Level Uncertainty Quantification for Calibrated Fine-Tuning (UQ4CT), which captures and calibrates uncertainty over the space of functions that map input prompts to outputs. We implement UQ4CT during the fine-tuning stage via a mixture-of-experts framework that hierarchically decomposes the functional space. Empirically, UQ4CT achieves over 25% reduction in Expected Calibration Error (ECE) while preserving high accuracy across five benchmarks. Even under distribution shift, UQ4CT maintains superior ECE performance with high accuracy, showcasing improved generalizability.

TL;DR: A method to quantify and calibrate uncertainty in LLMs with Mixture of Experts.