Why Large Language Models Do Not Understand What They Say

When a text-generation system produces the sentence “The cat sat on the mat,” it has performed a remarkable statistical feat. It has selected each word from thousands of candidates, arranged them according to patterns absorbed from billions of text samples, and delivered a grammatically correct string. What it has not done is form any conception of a cat, a mat, or the spatial relationship between them. This distinction—between producing coherent text and comprehending meaning—lies at the heart of one of the most persistent misunderstandings in contemporary computing.

The Illusion of Comprehension

Anyone who has interacted with a large-scale text generator has probably felt the uncanny sensation of being understood. You pose a question; the system responds with what appears to be thoughtful, contextualized prose. It references your specific wording, builds an argument, even anticipates objections. The intuitive leap is to attribute understanding to the source of such text. We are, after all, creatures who associate fluent language with conscious thought.

Yet this attribution reveals more about us than about the system. Humans are compulsive interpreters of meaning. Presented with even minimally coherent language, we fill in intentions, beliefs, and understanding. The anthropologist Stewart Guthrie argued decades ago that we project agency onto the world as a default cognitive strategy—it is safer to mistake a shadow for a person than a person for a shadow. When a machine strings together words that look like they emerged from understanding, we reflexively supply the understanding ourselves.

What Does “Understanding” Require?

Before examining what these systems lack, we should specify what genuine understanding demands. Philosophers and cognitive scientists have proposed several necessary conditions:

• Semantic grounding: Words must connect to something beyond themselves—to sensory experience, to physical interaction with the world, or at minimum to other concepts that are themselves grounded.
• Compositional representation: The meaning of “The cat sat on the mat” must be built from the meanings of its parts combined according to structural rules, not merely retrieved as a statistical approximation of similar strings.
• Causal reasoning: Understanding implies the ability to reason about why things happen, what could have happened instead, and what would follow from hypothetical changes.
• Intentional states: A system that understands what it says possesses beliefs about the content of its utterances and can act on those beliefs.

None of these conditions are met by transformer-based text generators. They operate on a fundamentally different principle—one that produces convincing output without any of the underlying apparatus that makes human language meaningful.

How Pattern Prediction Actually Works

At its core, a large text model is a statistical engine trained to predict which token comes next given a sequence of preceding tokens. During training, the system processes enormous corpora of text, adjusting billions of internal parameters to minimize prediction error. The result is a model that captures, with impressive fidelity, the distributional properties of language—the fact that “bank” is more likely to appear near “river” or “financial” than near “toaster” or “asteroid.”

This distributional knowledge is genuine and often useful. The system learns that certain word combinations signal certain registers, that arguments in academic prose follow recognizable structures, that questions typically precede answers. It can exploit these regularities to generate text that satisfies our expectations for coherence.

But distributional patterns are not meanings. Knowing that “bank” co-occurs with “river” tells you nothing about what a river is—how it sounds, what it feels like to fall into one, why people fish beside them, or that they flow downhill. The system has a map of correlations; it lacks the territory entirely.

The Chinese Room, Updated

John Searle’s Chinese Room argument, introduced in 1980, remains the most durable illustration of this gap. Searle asked us to imagine a person who does not speak Chinese locked in a room with a rulebook. Chinese speakers outside pass in questions written in Chinese characters; the person inside looks up the characters in the rulebook, follows the instructions, and passes out Chinese characters that constitute correct answers. To the outside observers, the room appears to understand Chinese. The person inside understands nothing.

Modern text generators are Searle’s room made real—except there is no person inside, only the rulebook. The system processes input according to learned patterns and produces output that satisfies the expectations of Chinese speakers (or English speakers, or Python programmers). Nothing in this loop requires or produces comprehension. The Stanford Encyclopedia of Philosophy’s entry on the Chinese Room remains essential reading on this point.

The Symbol Grounding Problem

In 1990, cognitive scientist Stevan Harnad posed a question that cuts directly to the issue: how can a system of symbols acquire meaning? If “cat” is defined in terms of “feline,” and “feline” is defined in terms of “carnivore,” and so on through an infinite chain of definitions, meaning never contacts anything real. Symbols remain trapped in a self-referential loop—what Harnad called the symbol grounding problem.

Humans escape this loop through embodied experience. A child learns “cat” by seeing, hearing, touching, and being scratched by an actual cat. The word becomes grounded in sensorimotor experience. Even abstract concepts—”justice,” “entropy,” “melancholy”—derive their meaning partly from their connections to eventually-grounded terms.

Text generators are ungrounded by design. They have never seen a cat, never been scratched, never experienced the weight of a warm animal on their lap. Every concept they manipulate is defined only in relation to other concepts, all of which float free of any experiential anchor. The system knows where “cat” appears in relation to other words; it has no idea what a cat is.

Syntax Without Semantics

The distinction between syntax (rules for manipulating symbols) and semantics (the meaning those symbols carry) has been central to logic and philosophy of language since at least Frege. Text generators operate entirely at the syntactic level—they manipulate tokens according to statistical patterns without any access to what those tokens signify.

This is why the same system that produces a fluent paragraph about photosynthesis can, with slight prompt variation, confidently assert that plants eat sunlight through tiny mouths. The fluency of each output reflects the system’s mastery of syntactic patterns. The absurdity of the second output reveals that no semantic constraint is checking the generated text against an internal model of how the world works.

Fluency and accuracy are statistically correlated in the training data—good prose about photosynthesis is usually also correct prose about photosynthesis. But the correlation is imperfect, and the system has no mechanism to detect when it has drifted from the correlated region into territory that is fluent but false. It cannot sense the difference because it has nothing to sense with.

Why This Matters Practically

The philosophical distinction between producing text and understanding it would be merely academic if not for the growing deployment of these systems in contexts where understanding matters. When people use generated text for medical advice, legal reasoning, or technical documentation, they are relying on outputs produced by a system that has no comprehension of health, law, or engineering.

The risks are compounded by what researcher Murray Shanahan calls “stalactites of plausibility”—extended passages of text that appear coherent locally but gradually drift into fabrication. Because the system lacks any semantic anchoring, there is no internal check that says “wait, this cannot be right.” Each next-token prediction is locally reasonable; the accumulating result can be globally nonsensical.

Understanding what these systems cannot do is not a dismissal of their capabilities. Pattern-based text generation has genuine applications—drafting boilerplate, suggesting completions, translating between languages where the mapping is well-represented in the training data. The danger arises precisely when we mistake pattern for comprehension and assign trust that the underlying architecture cannot support.

FAQ

Doesn’t the system’s ability to answer follow-up questions prove it understands the topic?

No. Follow-up responses are generated using the same pattern-prediction mechanism as initial responses, now conditioned on a longer context window. The system maintains a statistical representation of the conversation so far and predicts what a coherent continuation would look like. This is impressive pattern completion, but it does not require the system to have any internal model of the topic being discussed. A well-trained system can sustain a lengthy exchange about quantum mechanics while possessing zero comprehension of physics.

Could understanding emerge from a sufficiently large model trained on enough text?

This remains one of the most debated questions in the field. Proponents of “emergent understanding” argue that at sufficient scale, the statistical model must implicitly represent aspects of meaning. Skeptics counter that no amount of pattern matching over text can bridge the gap between syntax and semantics—grounding requires interaction with something beyond text. Current evidence is ambiguous: very large models show surprising capabilities that resemble understanding in narrow contexts, but they also show failures that no understanding system would make, such as confidently describing the taste of foods that do not exist.

If the system doesn’t understand what it says, why should we trust any of its outputs?

Trust is the wrong framework. A better approach is to treat generated text as a form of highly processed information—useful in contexts where you can independently verify accuracy, where the cost of error is low, or where the task requires fluency rather than comprehension. A calculator does not understand arithmetic, but we trust its outputs because its architecture reliably produces correct results for the operations it was built to perform. The challenge with text generators is that their failure modes are less predictable and less visible than a calculator’s, making independent verification all the more important.