Title: Words That Make Language Models Perceive
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: platonic representation hypothesis, perception, large language models

Abstract: Large language models (LLMs) trained purely on text ostensibly lack any direct perceptual experience, yet their internal representations are implicitly shaped by multimodal regularities encoded in language. We test the hypothesis that explicit sensory prompting can surface this latent structure, bringing a text‑only LLM into closer representational alignment with specialist vision and audio encoders. When a sensory prompt tells the model to 'see' or 'hear', it cues the model to resolve its next‑token predictions as if they were conditioned on latent visual or auditory evidence that is never actually supplied. Our findings reveal that lightweight prompt engineering can reliably activate modality‑appropriate representations in purely text‑trained LLMs.

TL;DR: A simple cue like asking the model to ‘see’ or ‘hear’ can push a purely text-trained language model towards the representations of a purely image-trained and purely-audio trained encoders.