Large language models produce language that is:
- fluent,
- contextually appropriate,
- and often indistinguishable, in local stretches, from human discourse.
In interaction, they sustain coherence across turns.
It is therefore not surprising that a claim arises—sometimes cautiously, sometimes not:
the model understands.the model means what it says.
Even when these claims are resisted, they tend to return in weaker forms:
- “it has a kind of understanding,”
- “it uses language meaningfully,”
- “meaning emerges from use.”
This is not carelessness.
It is a response to a genuine phenomenon:
output that behaves as if it were meaningful.
1. The Strength of the Appearance
The appearance of meaning in LLM output rests on several observable properties:
- coherence: responses are internally structured and contextually aligned
- sensitivity: output shifts appropriately with prompts
- generativity: novel formulations are produced, not retrieved verbatim
- continuity: discourse can be sustained over multiple turns
These are not trivial.
They exceed:
- simple pattern matching,
- fixed rule systems,
- and pre-scripted responses.
From the perspective of interaction:
the system participates in discourse in ways that are recognisably meaningful.
2. The Temptation to Attribute Meaning
Given this, the attribution of meaning follows a familiar path.
If:
- language use appears appropriate,
- responses are relevant,
- and behaviour is coherent,
then it is tempting to conclude:
meaning must be present.
This inference is reinforced by:
- the absence of visible mechanism,
- the scale and flexibility of responses,
- and the ease with which the system fits into existing communicative practices.
The result is a slide:
- from appearance of meaning
- to presence of meaning
without a clear account of what bridges the two.
3. The Reductionist Counter-Move
Against this, a common response is:
“it’s just statistics.”
On this view:
- the model predicts token sequences,
- based on patterns in training data,
- with no understanding, intention, or meaning.
This move attempts to block the attribution entirely.
But it does so by collapsing the phenomenon:
- coherence → probability
- responsiveness → pattern matching
- discourse → sequence generation
This is not wrong at the level of mechanism.
But it is insufficient at the level of description.
Because:
it does not explain why the output appears meaningful.
4. The Oscillation
As a result, discourse around LLMs oscillates:
- between attribution (“it understands”)
- and reduction (“it’s just statistics”)
Neither position stabilises.
- attribution overreaches,
- reduction under-describes.
And so the conversation cycles:
- meaning is granted, then withdrawn, then partially restored.
This instability is not accidental.
It reflects a deeper confusion:
the lack of a clear distinction between structure, value, and meaning.
5. What Is Actually Being Observed
What is directly observable in LLM output is:
- structured language,
- organised in ways that align with human expectations,
- responsive to prompts,
- and capable of sustaining discourse.
This is:
- highly constrained structure,
- dynamically produced,
- under conditions of interaction.
It is not:
- access to an internal state of meaning,
- nor evidence of construal within the model itself.
But neither is it trivial.
6. The Missing Condition
The inference from output to meaning depends on a missing step:
the assumption that producing language appropriately entails construal.
This assumption is rarely stated.
But it is doing all the work.
Because:
- meaning requires that something is construed as something,
- within a semiotic organisation.
The question, therefore, is not:
- whether the output is coherent,
but:
whether construal is present.
7. Holding the Problem Open
At this stage, no conclusion is required.
Two facts must be held together:
- LLM output exhibits properties strongly associated with meaningful discourse.
- The presence of these properties does not, by itself, establish the presence of meaning.
The gap between these two is the problem.
Closing Formulation
When output looks like meaning, the temptation is to treat it as meaning.
But appearance is not a criterion.
Coherence, relevance, and responsiveness are necessary for meaning—but they are not sufficient.The question is not whether the system behaves meaningfully.
It is whether anything is being construed as anything at all.
This is the entry point.
Not a dismissal.
Not an attribution.
A suspension.
From here, we can begin to separate what is present from what is projected.
Next Post
“Structure Is Not Meaning: Why Pattern Does Not Construe”
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