mlsanityData to 14 Oct 2026

Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph

arXiv 2308.13534 ↗First posted 13 Aug 2023cs.CL cs.AIcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 23 of its 91 references that are in the map. First posted on arXiv 13 Aug 2023.

Builds on

23 of 91 references.

References · 23

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11 more
  1. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
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Cited by year

Citing papers per year: 2023: 1; 2024: 4.

5 papers.

  1. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 387
  2. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 172
  3. 2024Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERTcited by 9
  4. 2024Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based 5G Core Network Management and Orchestrationcited by 8
  5. 2024An Empirical Categorization of Prompting Techniques for Large Language Models: A Practitioner's Guidecited by 7