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arXiv · 2508.02901

SLIM-LLMs: Modeling of Style-Sensory Language RelationshipsThrough Low-Dimensional Representations

Osama Khalid·Sanvesh Srivastava·Padmini Srinivasan
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Abstract

Sensorial language -- the language connected to our senses including vision, sound, touch, taste, smell, and interoception, plays a fundamental role in how we communicate experiences and perceptions. We explore the relationship between sensorial language and traditional stylistic features, like those measured by LIWC, using a novel Reduced-Rank Ridge Regression (R4) approach. We demonstrate that low-dimensional latent representations of LIWC features r = 24 effectively capture stylistic information for sensorial language prediction compared to the full feature set (r = 74). We introduce Stylometrically Lean Interpretable Models (SLIM-LLMs), which model non-linear relationships between these style dimensions. Evaluated across five genres, SLIM-LLMs with low-rank LIWC features match the performance of full-scale language models while reducing parameters by up to 80%.

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Osama Khalid, Sanvesh Srivastava, Padmini Srinivasan. 2025-08-04. SLIM-LLMs: Modeling of Style-Sensory Language RelationshipsThrough Low-Dimensional Representations. https://arxiv.org/abs/2508.02901

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