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

arXiv: https://arxiv.org/abs/2401.13110 · HTML: https://mlsanity.com/paper/2401.13110/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 23 Jan 2024.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges](https://mlsanity.com/paper/2010.09337/) (2020)
- [Graying the black box: Understanding DQNs](https://mlsanity.com/paper/1602.02658/) (2016)
- [Visual Analytics for Explainable Deep Learning](https://mlsanity.com/paper/1804.02527/) (2018)
- [Explaining Hyperparameter Optimization via Partial Dependence Plots](https://mlsanity.com/paper/2111.04820/) (2021)
- [Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection](https://mlsanity.com/paper/2401.03737/) (2024)
- [HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)](https://mlsanity.com/paper/2309.16021/) (2023)
- [Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods](https://mlsanity.com/paper/2210.07222/) (2022)
- [A Prescriptive Learning Analytics Framework: Beyond Predictive Modelling and onto Explainable AI with Prescriptive Analytics and ChatGPT](https://mlsanity.com/paper/2208.14582/) (2022)
- [AI Trust in business processes: The need for process-aware explanations](https://mlsanity.com/paper/2001.07537/) (2020)

## Built on by (most cited first, 21 of 21)

- [MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents](https://mlsanity.com/paper/2502.00415/) (2025)
- [Are Large Language Models the New Interface for Data Pipelines?](https://mlsanity.com/paper/2406.06596/) (2024)
- [AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support](https://mlsanity.com/paper/2504.06771/) (2025)
- [Layered Chain-of-Thought Prompting for Multi-Agent LLM Systems: A Comprehensive Approach to Explainable Large Language Models](https://mlsanity.com/paper/2501.18645/) (2025)
- [Towards Transparent AI: A Survey on Explainable Large Language Models](https://mlsanity.com/paper/2506.21812/) (2025)
- [VirtualXAI: A User-Centric Framework for Explainability Assessment Leveraging GPT-Generated Personas](https://mlsanity.com/paper/2503.04261/) (2025)
- [Leveraging Large Language Models for Explainable Activity Recognition in Smart Homes: A Critical Evaluation](https://mlsanity.com/paper/2503.16622/) (2025)
- [XForecast: Evaluating Natural Language Explanations for Time Series Forecasting](https://mlsanity.com/paper/2410.14180/) (2024)
- [FairyLandAI: Personalized Fairy Tales utilizing ChatGPT and DALLE-3](https://mlsanity.com/paper/2407.09467/) (2024)
- [Human-Centric eXplainable AI in Education](https://mlsanity.com/paper/2410.19822/) (2024)
- [Monetizing Currency Pair Sentiments through LLM Explainability](https://mlsanity.com/paper/2407.19922/) (2024)
- [Interpretable Anomaly-Based DDoS Detection in AI-RAN with XAI and LLMs](https://mlsanity.com/paper/2507.21193/) (2025)
- [Composable Building Blocks for Controllable and Transparent Interactive AI Systems](https://mlsanity.com/paper/2506.02262/) (2025)
- [HumAIne-Chatbot: Real-Time Personalized Conversational AI via Reinforcement Learning](https://mlsanity.com/paper/2509.04303/) (2025)
- [Triadic Fusion of Cognitive, Functional, and Causal Dimensions for Explainable LLMs: The TAXAL Framework](https://mlsanity.com/paper/2509.05199/) (2025)
- [A Two-Stage LLM Framework for Accessible and Verified XAI Explanations](https://mlsanity.com/paper/2604.12543/) (2026)
- [Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk](https://mlsanity.com/paper/2608.08126/) (2026)
- [Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI](https://mlsanity.com/paper/2506.12240/) (2025)
- [ProfileXAI: User-Adaptive Explainable AI](https://mlsanity.com/paper/2510.22998/) (2025)
- [LLEXICORP: End-user Explainability of Convolutional Neural Networks](https://mlsanity.com/paper/2511.02720/) (2025)
- [MATCH: Engineering Transparent and Controllable Conversational XAI Systems through Composable Building Blocks](https://mlsanity.com/paper/2511.22420/) (2025)

## 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.