AINeutralarXiv – CS AI · 7h ago6/10
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When Probing Accuracy Saturates, Fragility Resolves: A Complementary Metric for LLM Pre-Training Analysis
Researchers introduce 'fragility' as a complementary metric to linear probing for analyzing large language model pre-training, addressing the limitation that probe accuracy saturates early in training and becomes insensitive to ongoing representational changes. By measuring activation noise tolerance levels, fragility reveals structural evolution in how models encode lexical versus compositional information across layers, demonstrating that data curation and architectural choices leave distinct signatures invisible to traditional accuracy metrics.