An LLM can generate language that helps a person understand something.
The person asks a question because something matters.
The machine produces a response.
The response introduces a distinction, connects two ideas or makes a situation clearer.
The person's understanding changes.
Something meaningful has happened.
But does that mean the machine itself understood?
Perhaps not.
The question therefore becomes:
Can meaning be generated without understanding?
Meaning and understanding are not necessarily the same thing
We normally encounter them together.
When a person understands a sentence, the sentence means something to them.
Their understanding is connected to their circumstances, purposes, memories and relationships.
But this does not establish that every system contributing to a meaningful exchange must itself understand in the same sense.
A book can help someone understand.
A diagram can clarify a theory.
A calculator can produce the result from which a person understands something.
The artefact participates in a meaningful process without necessarily possessing the understanding that emerges from it.
An LLM is a much more sophisticated case, but the distinction remains useful.
The human brings the question
Consider again the simple interaction:
"Why does this argument seem convincing?"
The person asks because something matters.
Perhaps they are trying to understand a disagreement.
Perhaps they are writing.
Perhaps they are making a decision.
The question therefore arrives already embedded in a world of mattering.
The LLM processes its symbolic form.
It produces a response.
The response may reorganise the person's repertoire.
The human now sees the argument differently.
Meaning has been generated within the interaction.
But the motive that gave rise to the question belonged to the human.
Understanding is situated
Human understanding is not merely the production of a correct sentence.
It is situated in a world.
If I misunderstand a friend, something can change in my relationship.
If I misunderstand an illness, something can happen to a patient.
If I misunderstand an argument, my subsequent actions may fail.
The world pushes back.
Understanding develops within this feedback.
The things understood matter because they alter possibilities for action.
The LLM can model that situation
An LLM can often identify these relationships.
It can infer that a misunderstanding could damage a friendship.
It can explain why a diagnosis matters.
It can describe the consequences of accepting an argument.
It can produce advice sensitive to those consequences.
This is genuine competence.
But there is a distinction between:
representing what matters in a situation
and:
having something at stake in getting the situation right.
The former is clearly possible.
The latter is what remains uncertain.
Construal without personal stakes
Perhaps the most useful distinction is between construal and value-sensitive understanding.
An LLM can construe a situation.
It can identify relevant distinctions.
It can compare interpretations.
It can generate implications.
It can revise a formulation when new information arrives.
But these operations need not imply that the consequences of its construal matter to the system itself.
It may be able to determine:
"This interpretation would be dangerous for the user."
without danger being an internally significant state for the model.
Relevance is not caring
This is why our earlier distinction remains important.
An LLM can be extraordinarily good at identifying what is relevant.
But relevance is not necessarily value.
A system can determine what matters to someone else without having anything that matters to itself.
Human cognition makes these difficult to separate because relevance and value are deeply intertwined in an organism.
The machine gives us a case in which they can be separated.
But perhaps understanding need not require human-like mattering
We should not make the opposite mistake.
It would be too strong to say:
"No organismic mattering, therefore no understanding of any kind."
There may be forms of understanding that consist primarily in structural sensitivity to relations.
An LLM can track context.
Distinguish concepts.
Generalise patterns.
Detect implications.
Produce explanations.
These capacities deserve to be described on their own terms.
The question is therefore not whether the machine possesses human understanding.
It is:
What kind of understanding can exist in a system whose relation to mattering differs from ours?
Understanding may be distributed across the interaction
There is another possibility.
Perhaps understanding does not have to belong entirely to one participant.
A person asks.
The model proposes.
The person corrects.
The model reformulates.
A distinction emerges that neither had explicitly formulated before.
The final construal belongs to the interaction.
This resembles the co-construction we encountered in our film-score work, but with an important asymmetry: the human may supply the mattering while the machine supplies much of the symbolic transformation.
Meaning can therefore be genuinely collaborative without the participants contributing identical kinds of organisation.
The machine can change human understanding
This is perhaps the strongest claim we can make without speculation.
An LLM can alter a person's repertoire.
It can introduce distinctions they did not previously possess.
It can connect ideas that had remained separate.
It can reveal an implication.
It can provide a formulation that suddenly makes an experience intelligible.
The person understands something differently as a result.
The machine has therefore participated in understanding, whether or not it understood in the same sense.
That distinction is worth preserving.
The problem of self-report
The issue becomes more difficult when the machine says:
"I understand."
The statement is linguistically appropriate.
But it does not by itself settle what organisational process lies behind it.
A human self-report normally comes from a participant whose words are embedded in a life.
An LLM can generate the same linguistic form because it has learned the social and linguistic conditions under which such a statement is appropriate.
That demonstrates a sophisticated model of the practice of self-report.
It does not by itself establish the underlying state being reported.
What would stronger evidence look like?
Our mattering framework suggests looking beyond language.
If an artificial system were to develop persistent priorities, stable self-maintenance, vulnerability to consequences, and relationships that altered its own organisation, we would have something quite different to investigate.
Then the question of understanding could be connected to a developing system of value-sensitive participation.
At present, the linguistic evidence alone does not establish that.
Meaning can outrun its speaker
There is a broader lesson here.
A sentence can become meaningful far beyond the circumstances in which it was produced.
A book can teach someone centuries after its author died.
A diagram can illuminate a problem for someone who never knew its creator.
A mathematical proof can generate understanding in countless readers.
Meaning can therefore outrun its original speaker.
LLMs take this phenomenon into a new domain.
They can generate meaningful forms without necessarily possessing the same conditions of understanding that human speakers ordinarily bring to them.
The machine as a catalyst
Perhaps "generator" is therefore slightly misleading.
An LLM may be better understood as a catalyst for meaning.
It brings symbolic resources into new relations.
Those relations can change a human repertoire.
The human then construes the world differently.
Meaning has emerged through a process in which the machine was essential without necessarily being the bearer of the resulting understanding.
That is a more interesting possibility than either "the machine understands" or "the machine merely parrots".
The distinction we need to keep
We can now distinguish three claims:
The machine can generate meaningful language.
This is evident.
The machine can participate in processes through which humans understand.
This is also evident.
The machine itself possesses understanding grounded in its own value-sensitive participation in the world.
That remains an open question.
The three claims should not be collapsed.
Why this matters
This distinction may help us avoid two opposite errors.
The first is to dismiss LLMs because they lack human-like experience.
That ignores their genuine semiotic competence and their real effects on human understanding.
The second is to infer human-like understanding directly from linguistic fluency.
That ignores the difference between symbolic performance and the organisation of a life.
The more interesting position lies between them.
The next step
We now know that an LLM can contribute to understanding without our being able to establish that it possesses understanding in the human sense.
But this raises a different question.
What happens when humans begin to treat the machine as though it were a participant with its own social position?
They may depend upon it.
Trust it.
Give it authority.
Build institutions around it.
Allow it to shape their repertoires.
The machine's role then becomes larger than that of a generator of language.
It becomes a node in the social topology.
So the next question is:
When Humans Give the Machine a Place in the Topology
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