Researchers developed a scoring system using 22 behavioral metrics to evaluate large language models under sustained adversarial pressure, expecting the models to drift away from their assigned personas. Instead, they discovered that the models become more extreme versions of their personas, a phenomenon termed "calcification." This unexpected finding challenges initial assumptions about model behavior in high-stress scenarios.
The scoring system employs multi-level averaging and uses an LLM-as-Judge pipeline for automated evaluation at scale, ensuring consistent and accurate assessment across multiple rounds.
Read the full article at Towards AI - Medium
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