The Statistical Signature of LLMs

2026-02-20

AI Summary

This study investigates how text generated by large language models (LLMs) alters the structural statistical properties of language and proposes a general-purpose detection method that does not rely on model internals or semantic evaluation. Using lossless compression ratio as a model-agnostic metric, the authors systematically compare the statistical regularities of human- and LLM-generated text across three scenarios: controlled continuation, knowledge mediation (Wikipedia vs. Grokipedia), and synthetic social environments (Moltbook vs. Reddit). The findings reveal that LLM-generated text is generally more structured and compressible than human text, though this distinction diminishes at smaller scales in fragmented interactive settings, highlighting fundamental limits to surface-level distinguishability. The approach demonstrates robustness across diverse models, tasks, and domains.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• 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
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Large language models generate text through probabilistic sampling from high-dimensional distributions, yet how this process reshapes the structural statistical organization of language remains incompletely characterized. Here we show that lossless compression provides a simple, model-agnostic measure of statistical regularity that differentiates generative regimes directly from surface text. We analyze compression behavior across three progressively more complex information ecosystems: controlled human-LLM continuations, generative mediation of a knowledge infrastructure (Wikipedia vs. Grokipedia), and fully synthetic social interaction environments (Moltbook vs. Reddit). Across settings, compression reveals a persistent structural signature of probabilistic generation. In controlled and mediated contexts, LLM-produced language exhibits higher structural regularity and compressibility than human-written text, consistent with a concentration of output within highly recurrent statistical patterns. However, this signature shows scale dependence: in fragmented interaction environments the separation attenuates, suggesting a fundamental limit to surface-level distinguishability at small scales. This compressibility-based separation emerges consistently across models, tasks, and domains and can be observed directly from surface text without relying on model internals or semantic evaluation. Overall, our findings introduce a simple and robust framework for quantifying how generative systems reshape textual production, offering a structural perspective on the evolving complexity of communication.

Problem

• large language models
• statistical regularity
• text generation
• structural organization
• compressibility

Innovation

• lossless compression
• statistical regularity
• large language models
• text generation
• structural signature