The Outputs of Large Language Models are Meaningless

2025-09-26

AI Summary

This paper addresses whether large language model (LLM) outputs possess literal semantic meaning, arguing that LLMs lack intentionality—the necessary condition for semantic content—and thus their outputs are inherently devoid of truth-evaluable meaning. Method: Drawing on philosophy of language—specifically theories of intentionality, semantic externalism, and internalism—the study employs conceptual analysis and thought experiments to systematically demonstrate that LLMs, lacking genuine mental representations and referential intent, cannot generate semantically meaningful utterances. Contribution/Results: The paper explains the illusion of meaning in LLM outputs as arising from statistical regularities in training data and human-driven contextual projection. It further clarifies how such semantically void outputs retain cognitive utility. Critically, the analysis engages and refutes potential objections from both externalist and internalist semantic frameworks, establishing a novel, philosophically rigorous cognitive framework for understanding LLMs that remains grounded in AI practice.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Philosophy and Ethics of AI: AI & Epistemology

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

In this paper, we offer a simple argument for the conclusion that the outputs of large language models (LLMs) are meaningless. Our argument is based on two key premises: (a) that certain kinds of intentions are needed in order for LLMs' outputs to have literal meanings, and (b) that LLMs cannot plausibly have the right kinds of intentions. We defend this argument from various types of responses, for example, the semantic externalist argument that deference can be assumed to take the place of intentions and the semantic internalist argument that meanings can be defined purely in terms of intrinsic relations between concepts, such as conceptual roles. We conclude the paper by discussing why, even if our argument is sound, the outputs of LLMs nevertheless seem meaningful and can be used to acquire true beliefs and even knowledge.

Problem

• LLM outputs lack literal meaning due to missing intentions
• Challenges semantic externalist and internalist counterarguments about meaning
• Explains why seemingly meaningful LLM outputs enable knowledge acquisition

Innovation

• Argues LLM outputs lack literal meaning
• Rejects semantic externalist and internalist responses
• Explains perceived meaningfulness despite theoretical conclusion