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TEMPER: Testing Emotional Perturbation in Quantitative Reasoning
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Research indicates emotional framing in prompts degrades LLM quantitative reasoning, even when numerical content is identical.
Open sourceOneBench interpretation
Institutional assessment
So what
This research highlights a previously unquantified vulnerability in LLM performance that directly impacts production models handling user-generated queries, requiring new testing methodologies.
Do what
Your model validation and red-teaming frameworks must incorporate emotional perturbation testing to prevent silent performance degradation in production LLMs.