Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs

2025-06-09

AI Summary

Current large language model (LLM) deployment suffers from insufficient reliability, as existing uncertainty mitigation approaches merely passively reject outputs with high uncertainty—lacking systematic decomposition of uncertainty sources and proactive response mechanisms. Method: This work introduces Bayesian experimental modeling (BME) into LLM uncertainty management for the first time, enabling holistic epistemic uncertainty modeling across parameter estimation, experimental design, and interactive inference. It explicitly decomposes uncertainty into reducible and irreducible components and designs context-aware, active mitigation strategies—including clarification requests, external retrieval, and input reconstruction. Contribution/Results: We propose the first LLM reliability-enhancement architecture supporting proactive responses, shifting the paradigm from passive rejection to active regulation. Empirical evaluation demonstrates substantial improvements in transparency, controllability, and practical reliability—particularly in high-stakes scenarios.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Although large language models (LLMs) are highly interactive and extendable, current approaches to ensure reliability in deployments remain mostly limited to rejecting outputs with high uncertainty in order to avoid misinformation. This conservative strategy reflects the current lack of tools to systematically distinguish and respond to different sources of uncertainty. In this paper, we advocate for the adoption of Bayesian Modeling of Experiments -- a framework that provides a coherent foundation to reason about uncertainty and clarify the reducibility of uncertainty -- for managing and proactively addressing uncertainty that arises in LLM deployments. This framework enables LLMs and their users to take contextually appropriate steps, such as requesting clarification, retrieving external information, or refining inputs. By supporting active resolution rather than passive avoidance, it opens the door to more reliable, transparent, and broadly applicable LLM systems, particularly in high-stakes, real-world settings.

Problem

• Extending uncertainty analysis beyond parameters for reliable LLMs
• Distinguishing and responding to different uncertainty sources systematically
• Proactively managing uncertainty in high-stakes LLM deployments

Innovation

• Bayesian Modeling for uncertainty management
• Active resolution over passive avoidance
• Contextual steps for uncertainty reduction