Bayesian Network-Based Uncertainty Handling Framework for Generative AI Large Language Models

Abstract

Large Language Models (LLMs) have significantly advanced Generative Artificial Intelligence by enabling human-like text generation, code generation, reasoning, and conversational capabilities. However, these models frequently suffer from uncertainty-related challenges such as hallucination generation, confidence ambiguity, inconsistent outputs, and lack of explainability. These issues arise because Large Language Models (LLMs) generate responses based on probabilistic pattern prediction rather than true semantic understanding. As a result, the generated content may sometimes be factually incorrect, logically inconsistent, or overconfident despite limited supporting evidence. Existing transformer-based architectures primarily depends on probabilistic token prediction without incorporating explicit uncertainty management mechanisms. This research proposes a Bayesian Network-Based Uncertainty Handling Framework designed to improve the reliability and interpretability of Generative AI systems. The proposed framework integrates Bayesian inference capability with Large Language Model outputs to estimate confidence scores, validate generated responses, and reduce hallucination probabilities. A probabilistic validation layer is introduced to analyze contextual dependencies and evaluate the likelihood of response correctness. The framework also enhances explainability through probabilistic reasoning and confidence calibration. The proposed approach can be applied in critical AI applications including education, healthcare, decision-support systems, and intelligent conversational agents.