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Large language models generate plausible but false statements, a failure known as hallucination.

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SUPPORTED
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Recorded sources
8 sources for · 0 against

Counts group repeated records of the same source within each side. They do not measure evidence strength or source independence.

Is it true that large language models generate plausible but false statements, a failure known as hallucination?

Yes. Multiple sources state directly that large language models generate plausible but false or unsupported statements, the failure called hallucination; several others confirm the same behavior in narrower settings such as clinical or medical models.

Verdict: SUPPORTED · 8 sources for, 0 against · Last checked

The private library was unavailable for some searches in this check. Missing library results are not evidence against the claim.

Evidence for · 8
Large language model
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Large language model. (n.d.). en.wikipedia.org. https://en.wikipedia.org/wiki/Large_language_model

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More for · 7
Hallucination as an Inevitable Byproduct of Intelligence in Large Language Models
2025
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Kim, M. H. (2025). Hallucination as an Inevitable Byproduct of Intelligence in Large Language Models. doi.org. https://doi.org/10.31234/osf.io/q2c94_v1

Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models
2025
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Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models. (2025). doi.org. https://doi.org/10.48550/arxiv.2508.01862

AI Hallucination as Operational Trust Failure: Evidence from MetaTruth and the Confidence Gap in Large Language Models
2026
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Magrini, A. (2026). AI Hallucination as Operational Trust Failure: Evidence from MetaTruth and the Confidence Gap in Large Language Models. doi.org. https://doi.org/10.2139/ssrn.7397258

Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior -
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Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior - PMC Lock. (n.d.). pmc.ncbi.nlm.nih.gov. https://doi.org/10.3389/frai.2025.1622292

Retrieval-Augmented Large Language Models for Clinical Decision Support: A Systematic Review of Hallucination Mitigation and Evidence Grounding
2026
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Barua, S. & Rodgers, C. B. (2026). Retrieval-Augmented Large Language Models for Clinical Decision Support: A Systematic Review of Hallucination Mitigation and Evidence Grounding. doi.org. https://doi.org/10.21203/rs.3.rs-9741159/v1

Med-StepBench: a hierarchical reasoning framework for evaluating hallucinations in medical vision-language models
2026
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Med-StepBench: a hierarchical reasoning framework for evaluating hallucinations in medical vision-language models. (2026). hal.science. https://doi.org/10.48550/arXiv.2605.10002

Fairness Testing in Retrieval-Augmented Generation: How Small Perturbations Reveal Bias in Small Language Models
2025
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Oliveira, M. V. d. S. d., Silva, J. d. A., & Fontão, A. d. L. (2025). Fairness Testing in Retrieval-Augmented Generation: How Small Perturbations Reveal Bias in Small Language Models. doi.org. https://doi.org/10.48550/arXiv.2509.26584

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