mlsanityData to 14 Oct 2026

Factuality Challenges in the Era of Large Language Models

arXiv 2310.05189 ↗First posted 8 Oct 2023cs.CL cs.AI cs.LGcited by 126

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Abstract (arXiv)

The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention. These incredibly useful, natural-sounding tools mark significant advances in natural language generation, yet they exhibit a propensity to generate false, erroneous, or misleading content -- commonly referred to as "hallucinations." Moreover, LLMs can be exploited for malicious applications, such as generating false but credible-sounding content and profiles at scale. This poses a significant challenge to society in terms of the potential deception of users and the increasing dissemination of inaccurate information. In light of these risks, we explore the kinds of technological innovations, regulatory reforms, and AI literacy initiatives needed from fact-checkers, news organizations, and the broader research and policy communities. By identifying the risks, the imminent threats, and some viable solutions, we seek to shed light on navigating various aspects of veracity in the era of generative AI.

Key facts. Cited by 126 papers in the map (data through 14 Oct 2026). Builds on 80 of its 176 references that are in the map. First posted on arXiv 8 Oct 2023.

Builds on

80 of 176 references.

References · 80

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38 more
  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
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  3. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 31; 2025: 52; 2026: 40.

126 papers.

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  1. 2024A Comprehensive Study of Knowledge Editing for Large Language Modelscited by 163
  2. 2024AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Callscited by 124
  3. 2024Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAGcited by 123
  4. 2025HalluLens: LLM Hallucination Benchmarkcited by 92
  5. 2025Retrieval-Augmented Generation with Conflicting Evidencecited by 63
  6. 2024CiteME: Can Language Models Accurately Cite Scientific Claims?cited by 48
  7. 2024Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Modelscited by 34
  8. 2024Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Languagecited by 31
  9. 2024Logically Consistent Language Models via Neuro-Symbolic Integrationcited by 30
  10. 2024Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspectivecited by 29
  11. 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
  12. 2024FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMscited by 27
38 more
  1. 2025SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakescited by 27
  2. 2025Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directionscited by 26
  3. 2024Claim Verification in the Age of Large Language Models: A Surveycited by 24
  4. 2025Multi-Agent Debate for LLM Judges with Adaptive Stability Detectioncited by 23
  5. 2023Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformationcited by 19
  6. 2024xFinder: Large Language Models as Automated Evaluators for Reliable Evaluationcited by 19
  7. 2024Retrieval-Augmented Generation with Estimation of Source Reliabilitycited by 19
  8. 2024Knowledge Editing in Language Models via Adapted Direct Preference Optimizationcited by 17
  9. 2024Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurementcited by 17
  10. 2023Adapting Fake News Detection to the Era of Large Language Modelscited by 14
  11. 2025Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Modelscited by 13
  12. 2026Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systemscited by 12
  13. 2025Information Suppression in Large Language Models: Auditing, Quantifying, and Characterizing Censorship in DeepSeekcited by 11
  14. 2025Test-Time Scaling in Reasoning Models Is Not Effective for Knowledge-Intensive Tasks Yetcited by 11
  15. 2025AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM Chatbotscited by 10
  16. 2024Customized large language models can outperform Community Notes in correcting misinformationcited by 9
  17. 2024Persona Setting Pitfall: Persistent Outgroup Biases in Large Language Models Arising from Social Identity Adoptioncited by 9
  18. 2025RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checkingcited by 9
  19. 2024AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Datacited by 8
  20. 2025Can Community Notes Replace Professional Fact-Checkers?cited by 8
  21. 2024Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysiscited by 7
  22. 2025Linguistic Nepotism: Trading-off Quality for Language Preference in Multilingual RAGcited by 7
  23. 2024Adversarial Style Augmentation via Large Language Model for Robust Fake News Detectioncited by 6
  24. 2024LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Contentcited by 6
  25. 2024Integration of Large Vision Language Models for Efficient Post-disaster Damage Assessment and Reportingcited by 6
  26. 2025Evaluating open-source Large Language Models for automated fact-checkingcited by 6
  27. 2025How Does Response Length Affect Long-Form Factualitycited by 6
  28. 2025RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learningcited by 6
  29. 2026Position: Human-Centric AI Requires a Minimum Viable Level of Human Understandingcited by 6
  30. 2024I Am Not Them: Fluid Identities and Persistent Out-group Bias in Large Language Modelscited by 5
  31. 2024How will advanced AI systems impact democracy?cited by 5
  32. 2024Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Controlcited by 5
  33. 2025Community Moderation and the New Epistemology of Fact Checking on Social Mediacited by 5
  34. 2025RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checkingcited by 5
  35. 2025A Multi-Task Evaluation of LLMs' Processing of Academic Text Inputcited by 5
  36. 2025Adversarial Attacks Against Automated Fact-Checking: A Surveycited by 5
  37. 2026The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truthcited by 5
  38. 2026EvidenceRL: Reinforcing Evidence Consistency for Trustworthy Language Modelscited by 5

50 most cited of 126 All 126 (JSON)