Enhancing Explainability in Large Language Models Through Belief Change: A Simulation-Based Approach

Artificial intelligence systems, particularly those deployed in high-stakes environments, require a high degree of transparency and explainability to ensure that their decisions can be understood and trusted. Traditional approaches to enhancing explainability often rely on post-hoc methods that fail to fully capture the internal reasoning processes of complex models. In this research, a novel integration of Belief Change Theory was employed to address this challenge, offering a systematic framework for belief revision that directly influences the decisionmaking process of the model. The proposed methodology was implemented in the Llama model, which was modified to incorporate belief revision mechanisms capable of handling contradictory information and generating coherent explanations. Through a series of simulations, the modified model demonstrated significant improvements in belief consistency, revision accuracy, and overall explainability, outperforming traditional models that lack integrated belief management systems. The findings highlight the potential of Belief Change Theory to not only enhance the transparency of AI systems but also provide a foundation for more dynamic and interactive forms of model interpretability. The research opens new avenues for the development of AI systems that are both powerful and accountable, paving the way for their adoption in critical decision-making contexts.