Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior ↔ hallucination
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The concept [hallucination] is the central subject of the paper [Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior], which investigates its origins through controlled experiments [1] and defines specific metrics like Prompt Sensitivity and Model Variability to quantify its causes [2]. The paper provides a formal analysis and classification of this phenomenon [3].
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Survey and analysis of hallucinations in large language models frontiersin.org 3 facts
claimThe paper 'Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior' was published in Frontiers in Artificial Intelligence on September 30, 2025, by authors Anh-Hoang D, Tran V, and Nguyen L-M.
claimThe authors of the 'Survey and analysis of hallucinations in large language models' define Prompt Sensitivity (PS) and Model Variability (MV) as metrics to quantify the contribution of prompts versus model-internal factors to hallucinations.
procedureThe authors of the paper 'Survey and analysis of hallucinations in large language models' conducted controlled experiments using open-source models and standardized prompts to classify hallucination origins as prompt-dominant, model-dominant, or mixed.