mlsanityData to 14 Oct 2026

Trapping LLM Hallucinations Using Tagged Context Prompts

arXiv 2306.06085 ↗First posted 9 Jun 2023cs.CL cs.AIcited by 34

Not on the map. Why?

Abstract (arXiv)

Recent advances in large language models (LLMs), such as ChatGPT, have led to highly sophisticated conversation agents. However, these models suffer from "hallucinations," where the model generates false or fabricated information. Addressing this challenge is crucial, particularly with AI-driven platforms being adopted across various sectors. In this paper, we propose a novel method to recognize and flag instances when LLMs perform outside their domain knowledge, and ensuring users receive accurate information. We find that the use of context combined with embedded tags can successfully combat hallucinations within generative language models. To do this, we baseline hallucination frequency in no-context prompt-response pairs using generated URLs as easily-tested indicators of fabricated data. We observed a significant reduction in overall hallucination when context was supplied along with question prompts for tested generative engines. Lastly, we evaluated how placing tags within contexts impacted model responses and were able to eliminate hallucinations in responses with 98.88% effectiveness.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). Builds on 5 of its 10 references that are in the map. First posted on arXiv 9 Jun 2023.

Builds on

5 of 10 references.

References · 5

  1. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 894
  2. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  3. 2023Jailbreaking ChatGPT via Prompt Engineering: An Empirical Studycited by 498
  4. 2023Sources of Hallucination by Large Language Models on Inference Taskscited by 169
  5. 2023A Categorical Archive of ChatGPT Failurescited by 168

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 17; 2025: 11; 2026: 2.

34 papers.

Sort:

By area:

  1. 2023Large Language Modelscited by 780
  2. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 269
  3. 2024Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMscited by 259
  4. 2023Can LLM-Generated Misinformation Be Detected?cited by 177
  5. 2023LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examplescited by 177
  6. 2024Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similaritiescited by 159
  7. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 49
  8. 2024AI Hallucinations: A Misnomer Worth Clarifyingcited by 34
  9. 2024Large Language Models are Efficient Learners of Noise-Robust Speech Recognitioncited by 31
  10. 2024A Survey of Useful LLM Evaluationcited by 29
  11. 2024Estimating the Hallucination Rate of Generative AIcited by 20
  12. 2024ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinementcited by 12
22 more
  1. 2024RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbotscited by 11
  2. 2024InCoRo: In-Context Learning for Robotics Control with Feedback Loopscited by 10
  3. 2025TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Taskscited by 9
  4. 2024Leveraging Graph Structures to Detect Hallucinations in Large Language Modelscited by 7
  5. 2023Insights into Classifying and Mitigating LLMs' Hallucinationscited by 6
  6. 2025Explainable XR: Understanding User Behaviors of XR Environments using LLM-assisted Analytics Frameworkcited by 6
  7. 2025Hallucination Detection: A Probabilistic Framework Using Embeddings Distance Analysiscited by 6
  8. 2024Large Language Models for the Automated Analysis of Optimization Algorithmscited by 5
  9. 2024CLUE: Concept-Level Uncertainty Estimation for Large Language Modelscited by 5
  10. 2025LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verificationcited by 5
  11. 2024Harnessing the Power of Semi-Structured Knowledge and LLMs with Triplet-Based Prefiltering for Question Answeringcited by 4
  12. 2025Autofocus Retrieval: An Effective Pipeline for Multi-Hop Question Answering With Semi-Structured Knowledgecited by 3
  13. 2024Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspectivecited by 2
  14. 2025Balancing Content Size in RAG-Text2SQL Systemcited by 2
  15. 2025Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulationscited by 2
  16. 2024Killer Apps: Low-Speed, Large-Scale AI Weaponscited by 1
  17. 2025HalluField: Detecting LLM Hallucinations via Field-Theoretic Modelingcited by 1
  18. 2026Finding the Signal in the Noise: An Exploratory Study on Assessing the Effectiveness of AI and Accessibility Forums for Blind Users' Support Needscited by 1
  19. 2026Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learningcited by 1
  20. 2024War Elephants: Rethinking Combat AI and Human Oversightcited by 0
  21. 2025Can Generative AI be Egalitarian?cited by 0
  22. 2025Flash-Fusion: Enabling Expressive, Low-Latency Queries on IoT Sensor Streams with LLMscited by 0