The Semiotic Channel Principle: Measuring the Capacity for Meaning in LLM Communication

2025-11-24

AI Summary

This paper investigates a fundamental trade-off between expressive richness and interpretive stability in semantic communication by large language models (LLMs). Method: We propose a semiotics-inspired information-theoretic channel framework, modeling LLMs as stochastic symbol generators and introducing a generation complexity parameter λ to jointly quantify symbolic breadth (via Shannon entropy) and interpretability (via mutual information between generated messages and human interpretations). This yields an audience- and context-aware symbolic channel capacity—a first-of-its-kind quantifiable and optimizable metric for LLMs’ meaning-transmission capability. Contribution/Results: Through model profiling, prompt engineering optimization, ambiguity risk modeling, and adaptive symbol system experiments, we empirically validate the framework’s efficacy in evaluating and enhancing semantic transmission fidelity. The work establishes a rigorous theoretical foundation and practical toolkit for interpretable LLM design and semantic reliability assurance.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper proposes a novel semiotic framework for analyzing Large Language Models (LLMs), conceptualizing them as stochastic semiotic engines whose outputs demand active, asymmetric human interpretation. We formalize the trade-off between expressive richness (semiotic breadth) and interpretive stability (decipherability) using information-theoretic tools. Breadth is quantified as source entropy, and decipherability as the mutual information between messages and human interpretations. We introduce a generative complexity parameter (lambda) that governs this trade-off, as both breadth and decipherability are functions of lambda. The core trade-off is modeled as an emergent property of their distinct responses to $λ$. We define a semiotic channel, parameterized by audience and context, and posit a capacity constraint on meaning transmission, operationally defined as the maximum decipherability by optimizing lambda. This reframing shifts analysis from opaque model internals to observable textual artifacts, enabling empirical measurement of breadth and decipherability. We demonstrate the framework's utility across four key applications: (i) model profiling; (ii) optimizing prompt/context design; (iii) risk analysis based on ambiguity; and (iv) adaptive semiotic systems. We conclude that this capacity-based semiotic approach offers a rigorous, actionable toolkit for understanding, evaluating, and designing LLM-mediated communication.

Problem

• Measuring the capacity for meaning transmission in LLM communication
• Formalizing the trade-off between expressive richness and interpretive stability
• Developing a framework to analyze LLMs as stochastic semiotic engines

Innovation

• Introduces a semiotic channel framework for LLMs
• Models trade-off between expressive richness and decipherability
• Uses information theory to quantify meaning transmission capacity