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Zach Studdiford

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#artificial intelligence Oct 2025

Uncovering the Computational Ingredients of Human-Like Representations in LLMs

Evaluating over 75 models on a triplet similarity task finds that instruction fine-tuning and larger attention head dimensionality are among the strongest predictors of human alignment, while activation function choice, multimodal pretraining, and parameter size have limited influence on alignment.

Zach Studdiford, Timothy T. Rogers, Kushin Mukherjee et al. · arXiv.org · 1 citation

#artificial intelligence Preprint Aug 2026

(V)LMs generalize beyond surface co-occurrence: Evidence from cross-modal number agreement

Language models learn about grammatical number primarily from co-occurrence, and show frequency effects as a result---sometimes taken to indicate that they do not learn abstract ``rules'', and are instead dependent on specific lexical items. Testing generalization with text stimuli alone cannot settle this debate, sinc...

Zach Studdiford, Kanishka Misra · 0 citations