A Low-Rank Space Captures Linear Alignment Between LLM and Human Neural Activity During Naturalistic Language Listening

Key Takeaway

LLM-brain alignment doesn't require full model representations; a tiny low-dimensional subspace captures most neural predictivity, and these dimensions differ from what PCA identifies as important, revealing that models align with brains through specific, interpretable features rather than their ...