AI evidence extraction

A Survey on Hallucination in Large Language Models: Definitions, Detection, and Mitigation

Seyed Mahmoud Sajjadi Mohammadabadi, Burak Cem Kara, Can Eyüpoğlu, Oktay Karakuş · Preprints.org · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

2/4

Quality (LMQS)

Evidence

Citations

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.20944/preprints202510.0540.v2

Methodology & findings

Study design

Narrative literature review synthesizing academic definitions, taxonomies, detection methods, and mitigation strategies for hallucinations in Large Language Models.

Main result

The study found that hallucinations have evolved from simple factual errors to more complex failures of internal consistency. Specifically, "hallucination relates to consistency with known or provided context, while factuality relates to correctness against external reality. Understanding this difference allows for more effective detection: internal consistency checks fundamentally differ from external fact-checking." The review identifies a shift in perspective where "hallucination refers to outputs that contradict verifiable real-world knowledge" and distinguishes this from failures in faithfulness to user-provided constraints.

Reports effect sizes.

Research paradigm

Positivist/Empiricist with descriptive-analytical approach

Author conclusions

The authors conclude that "handling hallucination doesn't mean eliminating it completely. It's about creating a strong, layered system of tools and practices that make LLMs reliable, clear, and safe for broad use in society." They emphasize that "the best approach involves a layered strategy called 'defense-in-depth.' This means using multiple techniques throughout the model's lifecycle. It starts with careful curation of high-quality, fact-based data (data-centric), continues with strong model alignment through preference optimization and targeted knowledge editing (model-centric), and finishes with real-time, evidence-based grounding during deployment (inference-time)."

Risk of bias

Selection bias in literature coverage - review does not indicate systematic search protocol or predefined inclusion criteria; Publication bias - only published/preprint papers likely included; Author bias - single-author review without independent screening; Scope limitation - focuses on academic literature, may miss industry reports or grey literature

Limitations

The authors acknowledge that "although we have made notable progress in spotting and reducing hallucinations, key trends and ongoing problems define the current research landscape." Additionally, they note that "Despite significant progress, several foundational challenges remain in hallucination research" including that "Scalable and High-Quality Data Curation: The principle of 'garbage in, garbage out' continues to be a major hurdle." Further, "The Alignment-Capability Trade-off: A significant issue is the risk of an 'alignment tax.' This happens when efforts to improve accuracy and reduce mistakes unintentionally weaken other important abilities of the model."

Open questions raised

Scalable and high-quality data curation at trillion-token scales
The alignment-capability trade-off: preventing 'alignment tax' where accuracy improvements weaken other model capabilities
Editing reasoning paths, not just discrete facts - moving beyond locate-then-edit approaches
Compositionality of mitigation techniques - understanding interactions between RAG, DPO, knowledge editing, and other methods
The inevitability of hallucination - research on managing rather than eliminating hallucinations through uncertainty estimation and robust detection
Mechanistic interpretability tools for tracing multi-hop reasoning pathways

Data: TruthfulQA (mentioned for ITI training)Extracted from: pdfAgreement 75%

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