Crossed random-effects models on LLM word ratings show 16.9% variance from genuine stimulus-specific individuality, exceeding null models and forming coherent per-model fingerprints.
Scott, Anne Keitel, Marc Becirspahic, Bo Yao, and Sara C
2 Pith papers cite this work. Polarity classification is still indexing.
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Fine-tuned LLM and explainable models predict vocabulary difficulty with correlations r > 0.91 and r > 0.77, showing spelling difficulty and test item construction as key influences in addition to word production difficulty.
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Machine individuality: Separating genuine idiosyncrasy from response bias in large language models
Crossed random-effects models on LLM word ratings show 16.9% variance from genuine stimulus-specific individuality, exceeding null models and forming coherent per-model fingerprints.
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Sakura at BEA 2026 Shared Task 1: What Makes Vocabulary Difficult?
Fine-tuned LLM and explainable models predict vocabulary difficulty with correlations r > 0.91 and r > 0.77, showing spelling difficulty and test item construction as key influences in addition to word production difficulty.