Unveiling Intrinsic Dimension of Texts: from Academic Abstract to Creative Story
This provides practical guidance for using ID in LLM analysis, addressing an underexplored area in understanding textual determinants, though it is incremental in grounding ID in text properties.
The study investigated how intrinsic dimension (ID) relates to interpretable text properties, finding that ID is uncorrelated with entropy-based metrics after controlling for length, shows genre stratification with scientific prose having low ID (~8) and creative writing high ID (~10.5), and identifies causal features like formal tone reducing ID and personalization increasing it.
Intrinsic dimension (ID) is an important tool in modern LLM analysis, informing studies of training dynamics, scaling behavior, and dataset structure, yet its textual determinants remain underexplored. We provide the first comprehensive study grounding ID in interpretable text properties through cross-encoder analysis, linguistic features, and sparse autoencoders (SAEs). In this work, we establish three key findings. First, ID is complementary to entropy-based metrics: after controlling for length, the two are uncorrelated, with ID capturing geometric complexity orthogonal to prediction quality. Second, ID exhibits robust genre stratification: scientific prose shows low ID (~8), encyclopedic content medium ID (~9), and creative/opinion writing high ID (~10.5) across all models tested. This reveals that contemporary LLMs find scientific text "representationally simple" while fiction requires additional degrees of freedom. Third, using SAEs, we identify causal features: scientific signals (formal tone, report templates, statistics) reduce ID; humanized signals (personalization, emotion, narrative) increase it. Steering experiments confirm these effects are causal. Thus, for contemporary models, scientific writing appears comparatively "easy", whereas fiction, opinion, and affect add representational degrees of freedom. Our multi-faceted analysis provides practical guidance for the proper use of ID and the sound interpretation of ID-based results.