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Large language models predict human sensory judgments across six modalities.

Raja Marjieh1, Ilia Sucholutsky2, Pol van Rijn3

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Large language models can extract significant perceptual information from text, mirroring human judgments in areas like color and sound. Even models without direct visual training show strong correlations with human perception, highlighting language

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Area of Science:

  • Cognitive Science
  • Philosophy of Mind
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • A long-standing challenge in cognitive science and philosophy is understanding how much perceptual information can be derived solely from language.
  • Investigating the relationship between linguistic representations and human perception is crucial for understanding cognition.

Purpose of the Study:

  • To determine the extent to which large language models (LLMs) can recover perceptual information from text.
  • To establish a lower bound on the perceptual information extractable from language using state-of-the-art LLMs.
  • To explore the impact of cross-linguistic variations on perceptual representations within LLMs.

Main Methods:

  • Elicited pairwise similarity judgments from GPT models across six diverse psychophysical datasets.
  • Compared LLM-generated judgments with human data to assess representational accuracy.
  • Applied LLMs to a multilingual color-naming task to analyze cross-linguistic perceptual variations.

Main Results:

  • LLM judgments showed significant correlations with human data across all tested domains, successfully recovering known perceptual structures like the color wheel and pitch spiral.
  • GPT-4, a model co-trained on vision and language, did not show modality-specific advantages, yielding similar results with or without direct visual input.
  • LLMs replicated known cross-linguistic variations in color naming (e.g., English vs. Russian), demonstrating an understanding of language-perception interactions.

Conclusions:

  • State-of-the-art LLMs can extract substantial perceptual information from language, serving as a valuable tool for cognitive science research.
  • Perceptual representations in LLMs are robust, correlating highly with human judgments even when relying solely on textual descriptions.
  • LLMs can model the influence of specific languages on perceptual categorization, offering insights into the interplay between language and perception.