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When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Research demonstrates spurious training data correlations drive LLM hallucinations and impair automated detection tools.
OneBench interpretation
Institutional assessment
So what
Superficial training data associations weaken standard hallucination detection frameworks used in enterprise model governance.
Do what
Review model validation protocols with the team responsible for model risk to test for training bias failure modes.