Building Trust in Conversational AI: A Comprehensive Review and Solution
Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge
Graph
- URL: http://arxiv.org/abs/2308.13534v1
- Date: Sun, 13 Aug 2023 22:47:51 GMT
- Title: Building Trust in Conversational AI: A Comprehensive Review and Solution
Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge
Graph
- Authors: Ahtsham Zafar, Venkatesh Balavadhani Parthasarathy, Chan Le Van, Saad
Shahid, Aafaq Iqbal khan, Arsalan Shahid
- Abstract summary: We introduce a comprehensive tool that provides an in-depth review of over 150 Large Language Models (LLMs)
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.
Our architecture adeptly blends linguistic sophistication with factual rigour and further strengthens data security through Role-Based Access Control.
- Score: 0.33554367023486936
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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