Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph. (arXiv:2308.13534v1 [cs.CL])

Conversational AI systems have emerged as key enablers of human-like
interactions across diverse sectors. Nevertheless, the balance between
linguistic nuance and factual accuracy has proven elusive. In this paper, we
first introduce LLMXplorer, a comprehensive tool that provides an in-depth
review of over 150 Large Language Models (LLMs), elucidating their myriad
implications ranging from social and ethical to regulatory, as well as their
applicability across industries. Building on this foundation, we propose a
novel functional architecture that seamlessly integrates the structured
dynamics of Knowledge Graphs with the linguistic capabilities of LLMs.
Validated using real-world AI news data, our architecture adeptly blends
linguistic sophistication with factual rigour and further strengthens data
security through Role-Based Access Control. This research provides insights
into the evolving landscape of conversational AI, emphasizing the imperative
for systems that are efficient, transparent, and trustworthy.