mlsanityData to 14 Oct 2026

Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models

arXiv 2507.02870 ↗First posted 6 Jun 2025cs.CLcited by 7

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Abstract (arXiv)

Edgar Allan Poe noted, "Truth often lurks in the shadow of error," highlighting the deep complexity intrinsic to the interplay between truth and falsehood, notably under conditions of cognitive and informational asymmetry. This dynamic is strikingly evident in large language models (LLMs). Despite their impressive linguistic generation capabilities, LLMs sometimes produce information that appears factually accurate but is, in reality, fabricated, an issue often referred to as 'hallucinations'. The prevalence of these hallucinations can mislead users, affecting their judgments and decisions. In sectors such as finance, law, and healthcare, such misinformation risks causing substantial economic losses, legal disputes, and health risks, with wide-ranging consequences.In our research, we have methodically categorized, analyzed the causes, detection methods, and solutions related to LLM hallucinations. Our efforts have particularly focused on understanding the roots of hallucinations and evaluating the efficacy of current strategies in revealing the underlying logic, thereby paving the way for the development of innovative and potent approaches. By examining why certain measures are effective against hallucinations, our study aims to foster a comprehensive approach to tackling this issue within the domain of LLMs.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 177 of its 204 references that are in the map. First posted on arXiv 6 Jun 2025.

Builds on

177 of 204 references.

References · 177

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38 more
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50 most cited of 177 All 177 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2026Hallucination Cascade: Analyzing Error Propagation in Multi-Agent LLM Systemscited by 12
  2. 2025Profile-Aware Maneuvering: A Dynamic Multi-Agent System for Robust GAIA Problem Solving by AWorldcited by 5
  3. 2025From "Aha Moments" to Controllable Thinking: Toward Meta-Cognitive Reasoning in Large Reasoning Models via Decoupled Reasoning and Controlcited by 4
  4. 2026Evo-Attacker: Memory-Augmented Reinforcement Learning for Long-Horizon Tool Attacks on LLM-MAScited by 3
  5. 2026The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safetycited by 2
  6. 2026LANCET: Neural Intervention via Structural Entropy for Mitigating Faithfulness Hallucinations in LLMscited by 0
  7. 2026MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoningcited by 0