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Pigeonholing: how bad prompts hurt models, causing collapse and mistakes
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Research identifies "pigeonholing," where unintentionally bad prompts degrade LLM performance and cause mode collapse, even without malicious intent.
Open sourceOneBench interpretation
Institutional assessment
So what
This research details how subtle, non-malicious prompt flaws lead to LLM performance degradation, directly impacting the reliability and auditability of enterprise AI deployments.
Do what
Your model validation and red-teaming teams need to expand their focus beyond adversarial attacks to include subtle, non-malicious prompt variations that trigger performance collapse.