mlsanityData to 14 Oct 2026

Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety

arXiv 2312.06798 ↗First posted 5 Dec 2023cs.AI cs.CL cs.LGcited by 14

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Abstract (arXiv)

Explainability and Safety engender Trust. These require a model to exhibit consistency and reliability. To achieve these, it is necessary to use and analyze data and knowledge with statistical and symbolic AI methods relevant to the AI application - neither alone will do. Consequently, we argue and seek to demonstrate that the NeuroSymbolic AI approach is better suited for making AI a trusted AI system. We present the CREST framework that shows how Consistency, Reliability, user-level Explainability, and Safety are built on NeuroSymbolic methods that use data and knowledge to support requirements for critical applications such as health and well-being. This article focuses on Large Language Models (LLMs) as the chosen AI system within the CREST framework. LLMs have garnered substantial attention from researchers due to their versatility in handling a broad array of natural language processing (NLP) scenarios. For example, ChatGPT and Google's MedPaLM have emerged as highly promising platforms for providing information in general and health-related queries, respectively. Nevertheless, these models remain black boxes despite incorporating human feedback and instruction-guided tuning. For instance, ChatGPT can generate unsafe responses despite instituting safety guardrails. CREST presents a plausible approach harnessing procedural and graph-based knowledge within a NeuroSymbolic framework to shed light on the challenges associated with LLMs.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 55 of its 90 references that are in the map. First posted on arXiv 5 Dec 2023.

Builds on

55 of 90 references.

References · 55

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  5. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  6. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  7. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  8. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  9. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  10. 2019Language Models as Knowledge Bases?cited by 1,936
  11. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  12. 2023Large Language Models are not Fair Evaluatorscited by 1,085
38 more
  1. 2022Red Teaming Language Models with Language Modelscited by 1,026
  2. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  3. 2023The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Onlycited by 763
  4. 2022Teaching Models to Express Their Uncertainty in Wordscited by 751
  5. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 748
  6. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 739
  7. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 584
  8. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 573
  9. 2023LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusioncited by 564
  10. 2020How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answeringcited by 445
  11. 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 365
  12. 2023PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimizationcited by 316
  13. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 308
  14. 2022Teaching language models to support answers with verified quotescited by 292
  15. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 287
  16. 2022The Alignment Problem from a Deep Learning Perspectivecited by 258
  17. 2023Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graphcited by 238
  18. 2023Evaluating the Moral Beliefs Encoded in LLMscited by 215
  19. 2021Efficient Large Scale Language Modeling with Mixtures of Expertscited by 175
  20. 2021Primer: Searching for Efficient Transformers for Language Modelingcited by 164
  21. 2023The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediationscited by 131
  22. 2022Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goalscited by 126
  23. 2022The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systemscited by 93
  24. 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 92
  25. 2022TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversationscited by 66
  26. 2023PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptionscited by 53
  27. 2023Using In-Context Learning to Improve Dialogue Safetycited by 42
  28. 2022SafeText: A Benchmark for Exploring Physical Safety in Language Modelscited by 41
  29. 2022On the Evaluation Metrics for Paraphrase Generationcited by 32
  30. 2021Knowledge-intensive Language Understanding for Explainable AIcited by 25
  31. 2022Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Postscited by 23
  32. 2021ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphscited by 22
  33. 2020Benchmark and Best Practices for Biomedical Knowledge Graph Embeddingscited by 21
  34. 2022Modeling Motivational Interviewing Strategies On An Online Peer-to-Peer Counseling Platformcited by 20
  35. 2023Improving Retrieval-Augmented Large Language Models via Data Importance Learningcited by 17
  36. 2023CLEVRER-Humans: Describing Physical and Causal Events the Human Waycited by 11
  37. 2023Process Knowledge-infused Learning for Clinician-friendly Explanationscited by 8
  38. 2023A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depressioncited by 8

50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 3; 2026: 4.

14 papers.

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  1. 2024WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensionscited by 10
  2. 2024Neuro-Symbolic AI for Military Applicationscited by 7
  3. 2024Neurosymbolic AI for Enhancing Instructability in Generative AIcited by 3
  4. 2024Neurosymbolic AI approach to Attribution in Large Language Modelscited by 3
  5. 2025Mental Health Equity in LLMs: Leveraging Multi-Hop Question Answering to Detect Amplified and Silenced Perspectivescited by 3
  6. 2024MARS: A neurosymbolic approach for interpretable drug discoverycited by 2
  7. 2025Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunitiescited by 2
  8. 2024Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AIcited by 1
  9. 2025Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practicecited by 1
  10. 2026NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Modelscited by 1
  11. 2024IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Beingcited by 0
  12. 2026EcoFair: Energy-Efficient Inference Routing for Edge AI under Data Degradationcited by 0
2 more
  1. 2026NEURON: A Neuro-symbolic System for Grounded Clinical Explainabilitycited by 0
  2. 2026Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessmentcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025ATA: A Neuro-Symbolic Approach to Implement Autonomous and Trustworthy Agents
  2. 2024DANA: Domain-Aware Neurosymbolic Agents for Consistency and Accuracy
  3. 2024Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine
  4. 2026The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
  5. 2026From Interpretability to Control: Insights from Six Years of the TrustNLP Workshop
  6. 2023Establishing Trustworthiness: Rethinking Tasks and Model Evaluation
  7. 2025Probing AI Safety with Source Code
  8. 2025Can You Trust an LLM with Your Life-Changing Decision? An Investigation into AI High-Stakes Responses