From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning

2025-05-21

AI Summary

This study investigates whether large language models (LLMs) achieve a human-like trade-off between semantic fidelity and compression efficiency in their internal representations. Method: We introduce the first quantitative framework grounded in rate-distortion theory and the information bottleneck principle, enabling systematic comparison of LLM embeddings against human categorization behavior on canonical cognitive benchmarks. Contribution/Results: We find that while LLMs capture coarse-grained, human-aligned concepts, they exhibit significantly weaker fine-grained semantic discrimination than humans. Their representations overemphasize statistical compression at the expense of semantic nuance and lack contextual adaptivity. These findings reveal a fundamental cognitive divergence between LLMs and humans in concept formation and establish the first information-theoretic, interpretable paradigm for evaluating and improving the semantic representational capacity of language models.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Humans organize knowledge into compact categories through semantic compression by mapping diverse instances to abstract representations while preserving meaning (e.g., robin and blue jay are both birds; most birds can fly). These concepts reflect a trade-off between expressive fidelity and representational simplicity. Large Language Models (LLMs) demonstrate remarkable linguistic abilities, yet whether their internal representations strike a human-like trade-off between compression and semantic fidelity is unclear. We introduce a novel information-theoretic framework, drawing from Rate-Distortion Theory and the Information Bottleneck principle, to quantitatively compare these strategies. Analyzing token embeddings from a diverse suite of LLMs against seminal human categorization benchmarks, we uncover key divergences. While LLMs form broad conceptual categories that align with human judgment, they struggle to capture the fine-grained semantic distinctions crucial for human understanding. More fundamentally, LLMs demonstrate a strong bias towards aggressive statistical compression, whereas human conceptual systems appear to prioritize adaptive nuance and contextual richness, even if this results in lower compressional efficiency by our measures. These findings illuminate critical differences between current AI and human cognitive architectures, guiding pathways toward LLMs with more human-aligned conceptual representations.

Problem

• Compare human and LLM semantic compression strategies
• Assess LLM ability to capture fine-grained human distinctions
• Identify biases in LLM vs human conceptual prioritization

Innovation

• Novel information-theoretic framework for comparison
• Rate-Distortion Theory and Information Bottleneck principle
• Quantitative analysis of token embeddings in LLMs