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A Survey of the State of Explainable AI for Natural Language Processing

arXiv 2010.00711 ↗First posted 1 Oct 2020cs.CL cs.AI cs.LGcited by 206

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Abstract (arXiv)

Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable. This survey presents an overview of the current state of Explainable AI (XAI), considered within the domain of Natural Language Processing (NLP). We discuss the main categorization of explanations, as well as the various ways explanations can be arrived at and visualized. We detail the operations and explainability techniques currently available for generating explanations for NLP model predictions, to serve as a resource for model developers in the community. Finally, we point out the current gaps and encourage directions for future work in this important research area.

Key facts. Cited by 206 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 25; 2022: 42; 2023: 49; 2024: 47; 2025: 32; 2026: 11.

206 papers.

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  1. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 831
  2. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  3. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 394
  4. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 348
  5. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 257
  6. 2022Can language models learn from explanations in context?cited by 248
  7. 2022ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoningcited by 175
  8. 2021Alignment of Language Agentscited by 167
  9. 2022A Review on Language Models as Knowledge Basescited by 156
  10. 2021Post-hoc Interpretability for Neural NLP: A Surveycited by 155
  11. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  12. 2022Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networkscited by 126
38 more
  1. 2021Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunitiescited by 125
  2. 2023AI Transparency in the Age of LLMs: A Human-Centered Research Roadmapcited by 119
  3. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  4. 2022XAI for Transformers: Better Explanations through Conservative Propagationcited by 100
  5. 2023Tracr: Compiled Transformers as a Laboratory for Interpretabilitycited by 99
  6. 2021Trustworthy AI: A Computational Perspectivecited by 93
  7. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  8. 2021Neuron-level Interpretation of Deep NLP Models: A Surveycited by 81
  9. 2022Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoningcited by 78
  10. 2023MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Modelscited by 73
  11. 2023Large Language Models for Supply Chain Optimizationcited by 61
  12. 2021Explanation-Based Human Debugging of NLP Models: A Surveycited by 53
  13. 2021A Review on Explainability in Multimodal Deep Neural Netscited by 46
  14. 2024Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Eracited by 45
  15. 2023Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistencycited by 42
  16. 2024TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley Value Estimationcited by 41
  17. 2022On the Explainability of Natural Language Processing Deep Modelscited by 37
  18. 2022SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Featurescited by 33
  19. 2024TimeX++: Learning Time-Series Explanations with Information Bottleneckcited by 32
  20. 2021Toward Explainable AI for Regression Modelscited by 30
  21. 2023AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulationcited by 30
  22. 2025Language Models Fail to Introspect About Their Knowledge of Languagecited by 28
  23. 2021The Eval4NLP Shared Task on Explainable Quality Estimation: Overview and Resultscited by 27
  24. 2022Entailment Tree Explanations via Iterative Retrieval-Generation Reasonercited by 26
  25. 2022ferret: a Framework for Benchmarking Explainers on Transformerscited by 26
  26. 2021Reliability Testing for Natural Language Processing Systemscited by 25
  27. 2021Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factorscited by 25
  28. 2022Human Interpretation of Saliency-based Explanation Over Textcited by 25
  29. 2022Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Expertscited by 24
  30. 2024On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMscited by 24
  31. 2024The Quest for the Right Mediator: Surveying Mechanistic Interpretability Through the Lens of Causal Mediation Analysiscited by 24
  32. 2025Self-Interpretability: LLMs Can Describe Complex Internal Processes that Drive Their Decisionscited by 23
  33. 2023Rationalization for Explainable NLP: A Surveycited by 22
  34. 2022ProtoTEx: Explaining Model Decisions with Prototype Tensorscited by 21
  35. 2022Challenges in Applying Explainability Methods to Improve the Fairness of NLP Modelscited by 20
  36. 2023Towards Trustworthy Explanation: On Causal Rationalizationcited by 19
  37. 2022Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detectioncited by 18
  38. 2025A Comprehensive Survey on Self-Interpretable Neural Networkscited by 18

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