# LLMs for Explainable AI: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2504.00125 · HTML: https://mlsanity.com/paper/2504.00125/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 144 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 30 of 61)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [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)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [Measuring Faithfulness in Chain-of-Thought Reasoning](https://mlsanity.com/paper/2307.13702/) (2023)
- [OLMo: Accelerating the Science of Language Models](https://mlsanity.com/paper/2402.00838/) (2024)
- [Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?](https://mlsanity.com/paper/2004.03685/) (2020)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning](https://mlsanity.com/paper/2212.07919/) (2022)
- [Benchmarking Cognitive Biases in Large Language Models as Evaluators](https://mlsanity.com/paper/2309.17012/) (2023)
- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2407.11005/) (2024)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [Mitigating Label Biases for In-context Learning](https://mlsanity.com/paper/2305.19148/) (2023)
- [Datasets for Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2402.18041/) (2024)
- [TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations](https://mlsanity.com/paper/2207.04154/) (2022)
- [WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference](https://mlsanity.com/paper/1802.03052/) (2018)
- [GLocalX -- From Local to Global Explanations of Black Box AI Models](https://mlsanity.com/paper/2101.07685/) (2021)
- [The Challenge of Crafting Intelligible Intelligence](https://mlsanity.com/paper/1803.04263/) (2018)

## Built on by (most cited first, 30 of 45)

- [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025)
- [Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey](https://mlsanity.com/paper/2504.14520/) (2025)
- [Quantifying Trust: Financial Risk Management for Trustworthy AI Agents](https://mlsanity.com/paper/2604.03976/) (2026)
- [Thinking Longer, Not Always Smarter: Evaluating LLM Capabilities in Hierarchical Legal Reasoning](https://mlsanity.com/paper/2510.08710/) (2025)
- [An Agentic Approach to Generating XAI-Narratives](https://mlsanity.com/paper/2603.20003/) (2026)
- [Agentic AI in 6G Software Businesses: A Layered Maturity Model](https://mlsanity.com/paper/2508.03393/) (2025)
- [Protect: Towards Robust Guardrailing Stack for Trustworthy Enterprise LLM Systems](https://mlsanity.com/paper/2510.13351/) (2025)
- [Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities](https://mlsanity.com/paper/2505.21191/) (2025)
- [A three-Level Framework for LLM-Enhanced eXplainable AI: From technical explanations to natural language](https://mlsanity.com/paper/2506.05887/) (2025)
- [Glaucoma Detection and Structured OCT Report Generation via a Fine-tuned Multimodal Large Language Model](https://mlsanity.com/paper/2510.02403/) (2025)
- [ATA: A Neuro-Symbolic Approach to Implement Autonomous and Trustworthy Agents](https://mlsanity.com/paper/2510.16381/) (2025)
- [When Reasoning Leaks Membership: Membership Inference Attack on Black-box Large Reasoning Models](https://mlsanity.com/paper/2601.13607/) (2026)
- [When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Making](https://mlsanity.com/paper/2602.04003/) (2026)
- [Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering](https://mlsanity.com/paper/2607.00972/) (2026)
- [SynthEHR-Eviction: Enhancing Eviction SDoH Detection with LLM-Augmented Synthetic EHR Data](https://mlsanity.com/paper/2507.07421/) (2025)
- [Protocode: Prototype-Driven Interpretability for Code Generation in LLMs](https://mlsanity.com/paper/2509.25247/) (2025)
- [Unified Threat Detection and Mitigation Framework (UTDMF): Combating Prompt Injection, Deception, and Bias in Enterprise-Scale Transformers](https://mlsanity.com/paper/2510.04528/) (2025)
- [From Explainability to Action: A Generative Operational Framework for Integrating XAI in Clinical Mental Health Screening](https://mlsanity.com/paper/2510.13828/) (2025)
- [Increasing AI Explainability by LLM Driven Standard Processes](https://mlsanity.com/paper/2511.07083/) (2025)
- [PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations](https://mlsanity.com/paper/2603.06485/) (2026)
- [Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs](https://mlsanity.com/paper/2604.21092/) (2026)
- [Flexible Tool Selection through Low-dimensional Attribute Alignment of Vision and Language](https://mlsanity.com/paper/2505.22146/) (2025)
- [GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models](https://mlsanity.com/paper/2505.24340/) (2025)
- [RACE-Align: Retrieval-Augmented and Chain-of-Thought Enhanced Preference Alignment for Large Language Models](https://mlsanity.com/paper/2506.02726/) (2025)
- [Deliberative Reasoning Network: An Uncertainty-Driven Paradigm for Belief-Tracked Inference with Pretrained Language Models](https://mlsanity.com/paper/2508.04339/) (2025)
- [Measuring Algorithmic Partisanship via Zero-Shot Classification and Its Implications on Political Discourse](https://mlsanity.com/paper/2510.01258/) (2025)
- [Two-Stage Voting for Robust and Efficient Suicide Risk Detection on Social Media](https://mlsanity.com/paper/2510.08365/) (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)
- [Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making](https://mlsanity.com/paper/2602.11924/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) offer a promising approach to enhancing Explainable AI (XAI) by transforming complex machine learning outputs into easy-to-understand narratives, making model predictions more accessible to users, and helping bridge the gap between sophisticated model behavior and human interpretability. AI models, such as state-of-the-art neural networks and deep learning models, are often seen as "black boxes" due to a lack of transparency. As users cannot fully understand how the models reach conclusions, users have difficulty trusting decisions from AI models, which leads to less effective decision-making processes, reduced accountabilities, and unclear potential biases. A challenge arises in developing explainable AI (XAI) models to gain users' trust and provide insights into how models generate their outputs. With the development of Large Language Models, we want to explore the possibilities of using human language-based models, LLMs, for model explainabilities. This survey provides a comprehensive overview of existing approaches regarding LLMs for XAI, and evaluation techniques for LLM-generated explanation, discusses the corresponding challenges and limitations, and examines real-world applications. Finally, we discuss future directions by emphasizing the need for more interpretable, automated, user-centric, and multidisciplinary approaches for XAI via LLMs.