mlsanityData to 7 Oct 2026

XAI for All: Can Large Language Models Simplify Explainable AI?

arXiv 2401.13110 ↗First posted 23 Jan 2024cs.AI cs.HCcited by 21

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Abstract (arXiv)

The field of Explainable Artificial Intelligence (XAI) often focuses on users with a strong technical background, making it challenging for non-experts to understand XAI methods. This paper presents "x-[plAIn]", a new approach to make XAI more accessible to a wider audience through a custom Large Language Model (LLM), developed using ChatGPT Builder. Our goal was to design a model that can generate clear, concise summaries of various XAI methods, tailored for different audiences, including business professionals and academics. The key feature of our model is its ability to adapt explanations to match each audience group's knowledge level and interests. Our approach still offers timely insights, facilitating the decision-making process by the end users. Results from our use-case studies show that our model is effective in providing easy-to-understand, audience-specific explanations, regardless of the XAI method used. This adaptability improves the accessibility of XAI, bridging the gap between complex AI technologies and their practical applications. Our findings indicate a promising direction for LLMs in making advanced AI concepts more accessible to a diverse range of users.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 20 of its 41 references that are in the map. First posted on arXiv 23 Jan 2024.

Builds on

20 of 41 references.

References · 20

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  4. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  6. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  7. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  8. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  9. 2023BloombergGPT: A Large Language Model for Financecited by 923
  10. 2022GitHub Copilot AI pair programmer: Asset or Liability?cited by 150
  11. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  12. 2020Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challengescited by 120
8 more
  1. 2016Graying the black box: Understanding DQNscited by 68
  2. 2018Visual Analytics for Explainable Deep Learningcited by 39
  3. 2021Explaining Hyperparameter Optimization via Partial Dependence Plotscited by 35
  4. 2024Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selectioncited by 29
  5. 2023HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)cited by 25
  6. 2022Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methodscited by 13
  7. 2022A Prescriptive Learning Analytics Framework: Beyond Predictive Modelling and onto Explainable AI with Prescriptive Analytics and ChatGPTcited by 11
  8. 2020AI Trust in business processes: The need for process-aware explanationscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 14; 2026: 2.

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  1. 2025MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agentscited by 16
  2. 2024Are Large Language Models the New Interface for Data Pipelines?cited by 12
  3. 2025AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Supportcited by 12
  4. 2025Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Modelscited by 10
  5. 2025Towards Transparent AI: A Survey on Explainable Large Language Modelscited by 7
  6. 2025VirtualXAI: A User-Centric Framework for Explainability Assessment Leveraging GPT-Generated Personascited by 5
  7. 2025Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluationcited by 5
  8. 2024XForecast: Evaluating Natural Language Explanations for Time Series Forecastingcited by 4
  9. 2024FairyLandAI: Personalized Fairy Tales utilizing ChatGPT and DALLE-3cited by 3
  10. 2024Human-Centric eXplainable AI in Educationcited by 3
  11. 2024Monetizing Currency Pair Sentiments through LLM Explainabilitycited by 2
  12. 2025Composable Building Blocks for Controllable and Transparent Interactive AI Systemscited by 1
9 more
  1. 2025Interpretable Anomaly-Based DDoS Detection in AI-RAN with XAI and LLMscited by 1
  2. 2025HumAIne-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learningcited by 1
  3. 2025Triadic Fusion of Cognitive, Functional, and Causal Dimensions for Explainable LLMs: The TAXAL Frameworkcited by 1
  4. 2026A Two-Stage LLM Framework for Accessible and Verified XAI Explanationscited by 1
  5. 2026Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Riskcited by 1
  6. 2025Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AIcited by 0
  7. 2025ProfileXAI: User-Adaptive Explainable AIcited by 0
  8. 2025LLEXICORP: End-user Explainability of Convolutional Neural Networkscited by 0
  9. 2025MATCH: Engineering Transparent and Controllable Conversational XAI Systems through Composable Building Blockscited by 0