  Aligning large language models (LLMs) behaviour with human intent is critical
for future AI. An important yet often overlooked aspect of this alignment is
the perceptual alignment. Perceptual modalities like touch are more
multifaceted and nuanced compared to other sensory modalities such as vision.
This work investigates how well LLMs align with human touch experiences using
the "textile hand" task. We created a "Guess What Textile" interaction in which
participants were given two textile samples -- a target and a reference -- to
handle. Without seeing them, participants described the differences between
them to the LLM. Using these descriptions, the LLM attempted to identify the
target textile by assessing similarity within its high-dimensional embedding
space. Our results suggest that a degree of perceptual alignment exists,
however varies significantly among different textile samples. For example, LLM
predictions are well aligned for silk satin, but not for cotton denim.
Moreover, participants didn't perceive their textile experiences closely
matched by the LLM predictions. This is only the first exploration into
perceptual alignment around touch, exemplified through textile hand. We discuss
possible sources of this alignment variance, and how better human-AI perceptual
alignment can benefit future everyday tasks.
