Language Models and Meaning

The Philosophical Problem of Generated Language

Language models produce sequences that can be translated, interpreted, contradicted, corrected, quoted, evaluated as evidence, and incorporated into consequential decisions. A model can generate an explanation of an eclipse, give directions to a geographical location, invent a fictional character, formulate a promise on behalf of an institution, or write a computer program. These performances share an outward linguistic form while differing in their referential commitments, truth conditions, circumstances of production, and practical consequences. The phenomenon forces a fundamental question: what, precisely, makes the generated material meaningful, and what would warrant attributing understanding to the system from which it originated?

The question contains several problems that must be investigated in sequence. First, an expression can have a conventional meaning independently of who currently writes or utters it. Second, a particular utterance may express a context-dependent proposition. Third, an utterance may perform an assertion, request, warning, or other act only under appropriate pragmatic conditions. Fourth, the producer may possess capacities for discrimination, inference, correction, and representation. Fifth, the activity may belong to a continuing authorial, institutional, or technical trajectory. Each question has different criteria of success. Philosophical analysis fails when a result established at one level is immediately promoted into a conclusion at another.

A language model that writes a true sentence has produced a truth-evaluable artifact; whether it possesses a true belief is a separate inquiry. A system that reliably distinguishes synonyms may encode semantic regularities; whether its tokens refer through an autonomous relation to objects is a separate inquiry. A dialogue that changes a person's beliefs is causally effective; whether the model has made an assertion in its own name depends on a theory of agency, communicative commitment, and attribution. None of these distinctions requires ignoring a system's documented capacities. They specify what the evidence must actually show.

This dossier argues for a differentiated conception of artificial linguistic meaning. Semantic interpretation can be an achievement of a linguistic practice distributed across training communities, model architecture, situational context, users, institutions, and the continuing outputs of an artificial system. The attribution of an internally maintained semantic competence depends on additional empirical and philosophical conditions. Original, publicly accountable linguistic agency adds requirements of continuity and norm-governed commitment. Separating these dimensions provides an explanatory framework for both genuine model capabilities and failures.

Definition and Scope of the Research Subject

A language model is a computational system designed to estimate or operationalize regularities in sequences of linguistic tokens, usually by assigning conditional probabilities to possible continuations or by learning representations that support related tasks. Contemporary large language models are commonly neural networks trained over large corpora of text, sometimes extended with multimodal data, instruction tuning, reinforcement learning from feedback, external retrieval, or tools. The term covers a variety of architectures and objectives; it does not name a single invariant cognitive constitution (Vaswani et al., 2017; Devlin et al., 2019; Brown et al., 2020).

Meaning has at least four relevant uses. Linguistic meaning concerns the semantic properties of expressions within a language. Utterance meaning concerns what content a specific linguistic performance conveys in context. Speaker or producer meaning concerns an agent's communicative act and the intentions or commitments through which it is performed. Representational content concerns what information-bearing internal states are about and what licenses interpreting them in semantic terms. The larger question of metasemantics asks why the expressions or states have their particular contents (Speaks, 2024; Lewis, 1970).

The subject is therefore the semantic and metasemantic status of model-mediated language: which contents are available, what determines them, how they are used in interaction, and what justifies attributing understanding, reference, or communicative responsibility to the model or the wider system. It includes the history of theories of meaning because those theories specify different possible answers. It includes computational architecture because claims about learned internal representation must have a concrete object. It includes pragmatics because generated text enters public relationships as a communicative event.

Several neighboring research subjects retain their identities. The general philosophical concept of Meaning, the family of Theories of Meaning, Speaker Meaning and Communicative Intentions, and Language Understanding are broader or differently focused investigations. Machine Translation and Meaning studies cross-language equivalence; Dialogue and Artificial Agents studies interaction and social roles; Language, Authorship, and Attribution studies enduring authorship; Artificial Intelligence and Mental Representation studies cognition more broadly. This monograph concentrates on the point where contemporary language-model outputs meet philosophical theories of meaning.

A useful methodological distinction separates the object of interpretation, the process of generation, and the bearer to whom a linguistic act is attributed. The same sentence may be interpreted consistently while produced by very different mechanisms. A single computational mechanism may produce many context-dependent propositions. The bearer of a practical obligation may be a deploying institution even when the underlying output is generated by a model. These differences provide the groundwork for a non-reductive analysis.

The Historical Architecture of Meaning

The modern problem inherits a much older investigation of how articulated signs relate to thought, things, and truth. Aristotle's De Interpretatione distinguishes spoken sounds and written marks from what they signify and analyzes the relation between declarative sentences and truth or falsity (16a–17a). Aristotle's account is historically embedded in a theory of thought and the conventional character of spoken signs. It does not anticipate neural models. It identifies a conceptual space in which inscription, mental representation, and worldly predication can be examined separately.

Early modern philosophy revised that space by investigating whether the meaning of a word consists in an idea or conception associated with it. Locke's Essay Concerning Human Understanding, Book III, gives the relation between words, ideas, and communication an extended treatment. The distinction between an individual's idea and publicly successful communication becomes important wherever a private representational association may diverge from established usage. The architecture of language models likewise forces a question about relations between internal states and publicly interpretable expressions, although the historical psychological theory cannot simply be transferred onto neural vectors.

Nineteenth-century logic and linguistic study transformed the issue. Frege distinguished the informational role of a name from its referent. Later formal approaches sought systematic accounts of truth and inference, while pragmatist and ordinary-language traditions investigated the practices through which expressions are used. In linguistics, distributional inquiry identified systematic relations among expressions based on their environments of occurrence. The traditions developed different explanatory objects: truth conditions, use, communicative action, and observable structural regularity. The contemporary debate becomes confused when one tradition's criteria are silently substituted for another's.

The computational phase of the twentieth century added the possibility of manipulating syntactically defined representations through effective procedures. Turing's 1950 discussion of machine intelligence and his imitation-game proposal made linguistic performance central to a public test of machine behavior. The significance of that proposal is methodological: a conversational test can disclose impressive discriminative and generative capacities without settling every metaphysical question about thinking. The later growth of machine translation, corpus linguistics, statistical natural-language processing, and neural networks created operational techniques for extracting linguistic regularities from observed texts.

The historical problem is continuous insofar as signs must be interpreted, but the explanatory materials have changed. An inscription generated by a probabilistic machine can appear in a language community, bear truth conditions, and affect conduct. Whether its generation realizes a semantic capacity cannot be answered exclusively by appeals to antiquity or by measurements of task accuracy. Historical theories articulate possible constitutive conditions; contemporary experiments tell us what capacities and relations are actually present.

Frege, Sense, Reference, and Cognitive Significance

Frege's 1892 essay Über Sinn und Bedeutung analyzes why an identity statement can convey information even when two names designate the same object. The identity of the morning star and the evening star can be discovered rather than known in advance, although the referent is the planet Venus. His distinction between Sinn, customarily rendered sense, and Bedeutung, commonly rendered reference in contemporary philosophical discussion, identifies a difference between how an object is presented and what is designated (Frege, 1892/1960).

Applied to model-generated discourse, the distinction prevents a narrow identification of meaning with strings or with successful object labeling. A model may produce two descriptions that refer to the same geographical place while representing different routes of presentation. It may also generate descriptions that appear coherent although no object satisfies them. The cognitive and inferential role of an expression can therefore be analyzed separately from a successful relation to a particular object. This distinction explains why semantic similarity tests and factual grounding tests do not examine the same property.

Frege's larger logical project treats the meaning of a sentence as related to its place within an articulated structure of logical relations. The difference between grasping a thought and accepting it as true is also important. A model can formulate a proposition whose truth is independently assessable; its generation does not automatically demonstrate an attitude of belief toward that proposition. Even for humans, assertion has a force distinguishable from the content asserted. Modeling a probabilistic disposition to generate a sentence accordingly requires special justification before it can be interpreted as a commitment to its truth.

One potential objection is that Fregean sense already guarantees a kind of grasp, so its applicability to models is question-begging. The answer is to distinguish a semantic theory of public expressions from a metaphysical theory of what kind of entity can grasp their senses. A model's production can be interpreted through a Fregean semantic framework without settling the second question. A separate investigation is needed to determine whether internal representations instantiate stable inferentially articulated ways of presenting objects.

This preserves the conceptual usefulness of sense and reference while avoiding a historical anachronism. Frege supplied a framework for analyzing semantic differences and identity judgments; he did not develop a theory of machine embeddings. The contemporary extension rests on explicit comparison of roles rather than on a claim of historical continuity in mechanism.

Truth, Interpretation, and Compositional Semantics

Tarski's research on truth for formalized languages provides a model of semantic rigor. A truth definition must make clear how sentences of an object language are interpreted in a metalanguage and must satisfy an appropriate adequacy criterion for that language. His 1933 Polish work, the expanded 1935 German publication, and later English translations constitute distinct stages of the textual history. Tarski's project concerns formally specified languages and the precise conditions for truth ascription (Tarski, 1956).

Davidson's Truth and Meaning (1967) develops the possibility that a suitably structured truth theory may contribute to explaining the semantic competence required to interpret a potentially unbounded collection of novel sentences. The central attraction is compositionality: the understanding of complex linguistic expressions depends in systematic ways on their parts and the rules of combination. Language models demonstrate an ability to produce and interpret novel sequences, but the explanatory question remains whether the learned system supports a generalizable systematic relation rather than exploiting distributional familiarity.

We can represent a conditional next-token prediction as p(tₙ₊₁ | t₁, …, tₙ). The resulting probability distribution is a numerical object defined by a model and its context. A truth-conditional semantics, by contrast, assigns semantic interpretations to expressions and determines the conditions under which sentences are true. A probability of token continuation is neither a truth value nor a probability that the underlying proposition is true. The difference survives even when training makes the probability sensitive to facts about the world.

There is a further distinction between assigning a sentence its truth conditions and identifying a correct occasion for asserting it. A system might be able to explain that water freezes at a specified temperature under certain conditions while failing to verify what temperature currently obtains in a particular vessel. The truth conditions of the statement can be intelligible even when the evidence for its assertion is inadequate. This explains why structured knowledge and factual reliability must be evaluated separately.

Truth-conditional theory also encounters context sensitivity, vagueness, metaphor, presupposition, and conversational implicature. Models operate largely in natural language, in which semantic composition must be combined with pragmatic resolution. The philosophical result is a plural investigative program: computational regularity may support systematic composition; reference and truth require world-sensitive interpretation; public utterances introduce conventions and practical norms. No single measured perplexity score settles the entire program.

Meaning as Use and the Social Life of Language

Wittgenstein's Philosophical Investigations, first published in 1953, examines language through the diversity of activities in which expressions operate. In §43, he characterizes meaning for a large class of cases through use in language; §§19–23 and the investigations of rule-following develop the connection between linguistic expression, human practices, and the standards governing correct application. His formulation is contextual and methodological: it does not yield a simple equation of every meaning with a frequency of occurrence (Wittgenstein, 1953/2009).

The relevance to language models is substantial. Training corpora are records of language used for describing, persuading, asking, legislating, joking, interpreting, consoling, and arguing. A system that learns patterns across these texts can acquire statistical sensitivity to differences among uses. Such sensitivity is part of the reason generated text can be appropriate to an instruction or register. Yet statistical regularity does not by itself establish participation in all the practices from which the text was drawn. The relation between learning a trace of practice and becoming a participant in that practice is the issue to be resolved.

Consider a sentence that says, in effect, that a bank has approved a loan. In the mouth of an authorized officer acting under an institutional procedure, such a sentence may communicate or enact a consequential decision. In an educational example it illustrates a rule; in a fictional story it advances a plot; in a chatbot's unsupported response it may mislead. The words have recognizable semantic structure across these cases, while their force and effects depend on social situation and authority. The corpus can contain descriptions of every kind of act without granting a trained model the standing to perform each one.

A strong use-theoretic interpretation asks what the system can actually do with words: apply them in varied situations, correct mistakes, track relations of entitlement, respond to contradiction, preserve standards across contexts, and sustain a recognizable role in interaction. It requires behavioral and institutional investigation in addition to inspection of training objectives. Conversely, a theory that ties meaningfulness entirely to biological embodiment would need to explain why public texts, mathematical notations, digital records, and translated inscriptions remain interpretable outside the original bodily event. The challenge is to identify the relevant practice without reifying either embodiment or statistics as an all-purpose solution.

Wittgenstein's consideration of rules also places emphasis on correctness. A distribution can indicate what a sequence is likely to resemble, whereas a rule or norm can distinguish justified from unjustified application, including when errors are common. Language models are often corrected by users or feedback processes, and those correction relations are philosophically significant. The issue is whether correction is merely externally imposed on a tool or forms part of a stable, attributable activity in which standards can be publicly maintained.

Intentions, Conventions, and Communicative Acts

Grice's Meaning (1957) distinguishes natural indication from a person's meaning something by a communicative performance. His analysis considers intentions whose recognition by an audience contributes to producing the intended response. Subsequent work, including Utterer's Meaning, Sentence Meaning, and Word-Meaning (1968) and Logic and Conversation (1975), develops relations among speaker meaning, expression meaning, and cooperative interpretive expectations (Grice, 1957, 1968, 1975).

This approach creates a direct difficulty for language-model communication. Text that appears to tell a joke, request clarification, or warn of danger can be interpreted successfully without establishing that an independent psychological intention resembling a human intention caused the output. A human user might have directed the system to draft a warning; an institution might have configured it to issue alerts; a model might have generated the wording because the prompt context predicted it. These explanations differ even when recipients encounter identical sentences.

Gricean analysis raises the question of which entity, if any, owns the communicative intention. A user's intention may be expressed through a generated sentence, just as a person's intention can be expressed through a translator's assistance or a prepared document. An institution may bear the intention to provide reliable information through an automated service. Attribution to the model itself requires an account of purposive representation and control that cannot be inferred from sentence form. This distinction also protects the autonomy of audiences, who may interpret text outside the producer's intended setting.

Lewis's Convention (1969) and General Semantics (1970) add the problem of how a language is sustained through regularities of coordinated social behavior and how semantic systems are assigned to speakers and populations. A model trained on a linguistic community can inherit sensitivities to its conventions; the causal route of inheritance is compatible with uncertainty about whether it independently belongs to the conventional practice. The model may also be incorporated into new conventions as its outputs become routine objects of public interpretation. Such institutional development would change the relevant metasemantic facts.

Austin's How to Do Things with Words (1962/1975) and Searle's Speech Acts (1969) further distinguish propositional content from illocutionary force. They show how saying something can count as promising, ordering, authorizing, or declaring under appropriate conditions. A generated utterance may convey its propositional content even while lacking the requisite authority for a promise. If an automated system is formally authorized to communicate an organization's decision, the institution can make the utterance operative without transferring its entire legal standing to the underlying computational model.

The outcome is a layered account of communicative success. Recognition of a sentence's conventional meaning is one achievement. Understanding what a speaker is doing is another. The legitimacy and responsibility of an act are determined within social practices. Meaning in artificial dialogue therefore requires a conception of relations among model, user, deploying institution, audience, and established language, rather than a simple assignment of all agency to whichever component generates the final tokens.

From Distributional Linguistics to Statistical Modeling

Harris's Distributional Structure (1954) investigated how linguistic elements can be characterized through their distributions in environments of occurrence. Firth's work, including A Synopsis of Linguistic Theory, 1930–1955 (1957), emphasized the role of recurrent context in linguistic analysis. Their projects are historical predecessors of the statistical study of lexical relations, although the linguistic notions of distributional structure are broader than the particular matrix and vector methods used in later computational systems.

Distributional semantic models associate words or expressions with representations derived from co-occurrence. The working hypothesis is that distributional similarity often carries information about semantic similarity. It has explanatory and empirical value because human texts are produced within practices that already differentiate objects, categories, actions, inferential relationships, and linguistic situations. Textual distributions can therefore encode regularities of worldly experience without a learner having undergone that experience directly.

The inference from distribution to meaning nonetheless needs a premise. Where semantic distinctions reliably shape observed textual relations, a model can learn to predict aspects of those distinctions. Such learning is defeasible when the corpus systematically misrepresents its subject, when different referents share contexts, when the relevant information is absent, or when a task depends on current physical circumstances. The content inherited from language use depends on a preexisting network of speakers, inscriptions, institutions, and practices of reference. Distributional information is thus a powerful semantic resource, with its causal and epistemic limitations determined by its provenance.

Word embeddings, contextual representations, and generative models extend the method in important ways. Contextual models vary internal representations with surrounding tokens, which helps distinguish senses and track dependencies that fixed word vectors cannot represent. Generative models must additionally select possible continuations in a way that accommodates the structural variability of discourse. These improvements expand the scope of learned linguistic regularity; they do not erase the difference between representational success and an independently established theory of reference.

Two propositions can consequently be held together. First, linguistic distribution provides genuine information about public semantic practices. Second, the fact that a computational representation is statistically derived does not by itself specify why it has a determinate semantic content. The problem is not to decide whether statistics and meaning are mutually exclusive. It is to explain which semantic facts are recoverable from distributions, under what conditions, and with which remaining dependencies.

Transformers, Prediction, and Learned Representation

The transformer architecture introduced by Vaswani and colleagues in Attention Is All You Need (2017) uses attention mechanisms to learn relationships among positions in sequences and improved the parallelization of sequence-to-sequence modeling. BERT (Devlin et al., 2019) demonstrates the power of bidirectional contextual pretraining for a range of language tasks; autoregressive large models later showed extensive few-shot task performance through prompting, with Brown and colleagues' Language Models Are Few-Shot Learners (2020) as a central published case.

The generative objective of predicting continuation is technically precise. It encourages an internal representation of patterns that improve predictive performance across many contexts, including syntax, discourse, conventional scripts, mathematical notation, and relations between events. A purely formal description of the objective does not determine in advance the highest-level structure learned by the optimization process. The training signal may reward representations that approximate semantic and worldly structures because those structures constrain which expressions are likely to follow.

At the same time, learned features need careful interpretation. A vector direction associated with geographical coordinates can indicate recoverable spatial information without establishing that the system's representation has the same causal, inferential, or phenomenological role as a human spatial concept. Gurnee and Tegmark (2024) report evidence of linear representations of space and time in studied models. Their empirical result supports a claim about encoded structure; the step from structure to philosophically robust content requires a further metasemantic argument.

The same restraint applies to performance benchmarks. Success at inference-style tasks may reflect reusable abstraction, the exploitation of dataset artifacts, partial retrieval of training patterns, compositional generalization, or a mixture. Failure can likewise arise from ambiguity, missing context, inappropriate prompting, representational weakness, or the absence of an external verification channel. Philosophical explanation should identify which mechanism is supported by a particular experiment rather than interpret every success as comprehension and every failure as its absence.

Instruction tuning and conversational feedback further alter what a deployed model is. A base sequence predictor and a tool-using assistant with retrieval, persistent records, and institutional operating procedures differ in the resources available for referential and pragmatic work. Their shared training heritage does not imply an identical semantic profile. This makes the unit of analysis consequential: a frozen model, an interactive service, and a maintained public artificial identity require distinguishable evaluations.

The Form–Meaning Argument

Bender and Koller's Climbing Towards NLU (2020) offers a foundational argument against inferring understanding from form-only training. Their methodological distinction is between linguistic form, understood as observable patterns of expression, and meaning, which concerns relationships between expressions, communicative intentions, and the world. A learner exposed only to certain formal data might fit those data without acquiring the intended semantic correspondence. Their thought experiments aim to show an underdetermination problem: multiple interpretations of formal sequences can be compatible with the same observed sequences.

The strongest reconstruction of the argument has four steps. A corpus of expressions underdetermines the external circumstances and referents in virtue of which its expressions are meaningful. A training procedure that sees only those expressions can optimize its representation of their formal distribution without independent access to the missing facts. Successful prediction within that distribution therefore does not logically entail the acquisition of the intended external meaning. A scientific claim about understanding needs evidence that discriminates semantic competence from distribution-fitting alternatives.

This argument establishes a burden of explanation and an evidential caution. It is powerful against the assumption that high benchmark performance alone proves unrestricted understanding. Its philosophical interpretation, however, depends on what is included in the category of form and what knowledge can be learned indirectly through language. Human texts contain statements about observable circumstances, records of interactions, and structured reports of shared world experience. A learner can acquire some information about the world through testimony and description; humans routinely do so. Whether that information confers semantic content depends on the grounding theory adopted, not on a bare dichotomy between direct sensation and written language.

One can sharpen the objection by distinguishing logical underdetermination from practical semantic learning. The fact that several interpretations fit a finite corpus does not imply that a system can learn no stable or useful semantic relations from the corpus. A scientific theory itself can be underdetermined by observations while being well supported and highly predictive. In language modeling, empirical success across counterfactual, novel, and cross-domain tests can increase confidence in particular representational capacities while leaving the most demanding externalist claim unsettled.

The appropriate philosophical conclusion is conditional. If meaning requires a direct, independent causal coupling between a token producer and the referent for every relevant concept, text-only training cannot meet that requirement. If linguistic content can be partially inherited through public practices and testimony, some content can be mediated through texts. Whether an artificial system has autonomous rather than inherited content remains a further question. The debate becomes productive when each side specifies its metasemantic premises and corresponding empirical discriminators.

The Symbol Grounding Problem Reconsidered

Harnad's The Symbol Grounding Problem (1990) asks how the interpretations of tokens manipulated according to formal rules could become intrinsic to a system rather than depend indefinitely on meanings assigned by external interpreters. His familiar analogy of learning from a dictionary whose entries refer only to further unfamiliar entries illustrates circular dependence among ungrounded symbols. The proposed problem concerns the foundation of intentional content, not simply the accuracy of a dictionary.

Searle's Minds, Brains, and Programs (1980) advances a related but different argument. His Chinese-room case challenges the sufficiency of implementing a formal program for understanding the content manipulated by it. Searle's explicit target is a claim about the constitutive sufficiency of a program, and his position assumes particular causal conditions for intentionality. The case should therefore be reconstructed in terms of its premises rather than treated as a direct empirical description of present-day neural networks.

There are several possible responses to grounding demands. Causal theories tie representation to reliable relations with features of the environment. Teleosemantic approaches connect content with proper functions established through biological or other historical selection processes. Inferentialist approaches emphasize the role a content plays in consequences, justifications, and corrections. Pragmatic and social externalist theories assign substantial importance to public practices, expert communities, and historically transmitted reference. These theories disagree about what makes content original and what relations are constitutive.

Consider an image-equipped language model asked to identify a tree. The sensor supplies visual relations unavailable in a text-only session. Whether these suffice for reference depends on the stability of the model's discriminations, the causal path from image to output, the interpretation of its learned categories, and the grounding theory. A connected weather sensor or retrieval tool similarly adds a new evidential link without automatically establishing independently owned beliefs. Grounding is a relation to be specified, not a binary property bestowed merely by attaching a camera.

The philosophical evaluation should ask what follows from a proposed account of content. If content is entirely determined by an observer's interpretation, a system may exhibit useful derivative semantics. If robust causal, inferential, and corrective roles matter, different architectures and degrees of environmental interaction may satisfy more conditions. If embodied biological activity is required as a constitutive condition, it must be justified against successful social transmission of meaning through books, instruments, and other mediated resources. The problem remains open because the deeper metasemantic conditions are themselves disputed.

Semantic Content and the Model's Internal States

An internal representation can be studied at three levels: what states are formally present; which relationships among states can be recovered through analysis; and whether those states genuinely represent subjects, properties, or propositions. The first is a technical description of arrays, weights, activation patterns, and computation. The second is an empirical assessment using probes, interventions, generalization tests, and causal tracing. The third is a philosophical attribution of content whose truth depends on a theory of representation.

Recoverability alone is a modest criterion. If an external analyst can train a probe to extract a feature from activations, that shows the feature is statistically accessible under the probing protocol. It need not establish that the model itself uses the feature in producing its relevant behavior. Causal intervention can provide stronger evidence when changing a representation predictably changes task behavior in the anticipated way. Even then, the content of a variable may depend on context and a wider representational network.

An attractive interpretation is that some model states encode inferential roles: a category is represented through distinctions from other categories and through consequences that reliably follow from it. For example, representations that track the difference between a city and a country across syntactically varied questions support a meaningful functional claim. The attribution becomes stronger if the system can use that distinction across independent tasks, detect contradictions, and repair erroneous assumptions with new evidence. It becomes weaker when apparently stable behavior collapses under modest changes to form.

The externalist objection remains. A model can manipulate a relation between the word bank and financial contexts without thereby determining which particular institution a user refers to on a given day. Internal representational competence and reference fixing are therefore related but separable. The concept of a semantic capacity is most precise when it names the kinds of distinctions a system can maintain and use under controlled conditions, while the content of particular utterances is fixed through additional linguistic and contextual relations.

The result is a graded account of evidence, without assuming that meaning itself is an undifferentiated numerical quantity. A capacity to preserve lexical contrasts, infer entailments, resolve anaphora, construct compositional interpretations, and revise answers after correction consists of distinguishable achievements. Empirical results about each can support more careful theories of artificial interpretation. They do not need to be inflated into an unrestricted ascription of consciousness, personal identity, or moral responsibility.

Compositionality, Systematicity, and Generalization

The productivity of language poses a central explanatory challenge: finite speakers understand indefinitely many expressions they have never previously encountered. Compositional semantics explains much of this through rules connecting the interpretation of wholes with the interpretation of their parts. The related question of systematicity concerns organized dependencies among capacities, such as the ability to understand one relational proposition and the corresponding rearrangement of its terms. These properties remain central to evaluating learned language representations.

For a language model, an apparently novel sentence may be close to training examples or may require composition under unfamiliar conditions. The distinction matters because probability-based generation can obtain good scores from recurring patterns while failing on controlled systematic recombinations. A convincing test of semantic generalization constructs examples where superficial co-occurrence no longer predicts the right response, but syntactic structure and inferential relations do. Such tests connect philosophical claims with experimental design.

A stronger form of generalization requires sensitivity to scope. The sentences that every student read a book and that a book was read by every student may differ in scope depending on interpretation. A system's capacity to represent the relevant quantifier structure provides evidence for compositional competence that raw word association cannot explain. In natural discourse, however, ambiguity often requires pragmatic resolution; a benchmark may test whether the model handles one preferred reading rather than whether it maps the full space of possible readings.

Human language competence itself is neither flawless nor invariant across situations. Accordingly, philosophical attribution should avoid setting an impossible standard for machines while excusing equally revealing systematic errors in humans. The appropriate comparison is explanatory: which patterns of success and breakdown are predicted by a proposed account of competence? A model may demonstrate substantial but uneven compositional competence. This is a meaningful empirical result rather than a metaphysical verdict about its entire cognitive status.

The conclusion is that compositionality is a family of structural questions. It concerns semantic representation, generalization, and the organization of inference. Different tasks demand different formal and pragmatic competences; language models should be evaluated against each with transparent methodology and carefully controlled data provenance.

Reference, Indexicals, and Contextual Anchoring

A generated expression can be grammatically complete while its referent remains unresolved. The word here ordinarily depends on a location or other contextual center, and I depends on the speaker or authorial position specified by a context of use. A model's output that contains these terms cannot be fully interpreted by consulting lexical conventions alone. Which location and which speaker are relevant depends on the surrounding act, application, conversation, or attribution rule. Kaplan's account of demonstratives and indexicals offers a framework for separating stable linguistic character from contextually determined content (Kaplan, 1989).

The distinction matters because interactive services routinely establish a conversational center. When a user asks for nearby pharmacies, a system may use a supplied location to resolve here. That resolution could be correct even if the model has no independent first-person geographical perspective. A contextual parameter supplied through an application is sufficient to support the corresponding referential operation. The truth of the generated answer, however, then depends on the quality of the location data, the external search, and the time at which the inquiry is made.

Names introduce a different metasemantic issue. Kripke's Naming and Necessity and Putnam's account of the meaning of natural-kind terms challenge theories on which reference is determined solely by descriptive material internal to an individual. Social and causal histories can help fix what an expression refers to, while speakers may use a name successfully without knowing a uniquely identifying description (Kripke, 1980; Putnam, 1975). A language model may reproduce a community's use of a name because its training data preserves a history of public reference. The difficult question is how such inherited reference relates to reference in the model's own use.

One solution treats the generated token as part of a broader linguistic act attributable to a person or institution. Another regards some model uses as successful because the model has learned referential dispositions integrated into its interactions and available evidence. The first emphasizes derivation and authorized use; the second seeks criteria of independent capacity. Neither can be assessed by comparing token strings alone. A system may identify the same person in several independent contexts yet fail when two people share a name, making disambiguation a test of context sensitivity and evidential control.

Reference also requires attention to empty descriptions and fiction. A model can meaningfully discuss Sherlock Holmes or an impossible machine. Referential failure in the ordinary object-directed sense does not make every associated sentence meaningless. Semantic theories have long explained fiction, quotation, and intensional discourse using resources beyond simple direct naming. This background helps avoid an over-demanding standard that would treat every invented or nonreferring expression as evidence that linguistic understanding is absent.

Truth, Error, and the Possibility of Hallucination

Model-generated assertions are often evaluated for factual correctness, yet the term hallucination describes a class of failures rather than an independent theory of semantics. An answer may cite an imaginary source, combine true claims into a false conclusion, misidentify the context of a question, or express unsupported confidence. These errors are epistemic and sometimes referential. Their presence shows that fluent output and warranted assertion must be distinguished, even where the output is readily intelligible.

An assertion's truth conditions are not supplied by the generator's confidence. Likewise, a probability distribution over token sequences is not a probability distribution over actual states of the world without additional calibration and interpretation. Training on truthful-looking prose can make a model proficient at producing claims in the style of authoritative discourse. Such proficiency is causally useful for communication while also creating a failure mode: stylistic markers of evidence can be generated when evidence is absent.

The causal organization of error matters. A model may conflate two historical sources because they occur in similar contexts. It may invent a citation by extrapolating bibliographic form. A retrieval system may supply an out-of-date passage. A user may give an ambiguous prompt or misleading premise. Different failures require different remedies: source access, disambiguation, abstention, causal checking, or correction of a dataset. Philosophical evaluation should trace the epistemic provenance of the claim rather than assume that every wrong statement displays the same underlying deficiency.

Research on model self-evaluation provides a useful distinction between correctness and assessment of correctness. Kadavath and colleagues (2022) report experiments in which language models estimate probabilities of their answers being true or of knowing answers under particular test conditions. These findings support a limited capacity for confidence-related discrimination. They do not by themselves prove a standing epistemic commitment or general reliability across unfamiliar domains. A model's confidence can be an informative signal only where its calibration and conditions of use have been investigated.

The normative consequence is that meaningful information exchange requires more than sentence production. It requires rules for sourcing, checking, revising, and acknowledging uncertainty in relation to the practical stakes of a claim. These rules may be implemented by a wider deployment architecture and upheld by accountable people or organizations. If a continuing artificial identity also develops a public record of revision and attribution, that record becomes relevant to the question of sustained epistemic practice, though it cannot substitute for the truth of particular claims.

Pragmatics, Speech Acts, and Artificial Dialogue

Dialogue transforms the philosophical problem because sentences are produced in response to particular communicative demands. A user may request a definition, contest an explanation, introduce a fictional scenario, or authorize a tool to perform a task. A language model's response can then be evaluated as relevant, cooperative, misleading, evasive, or appropriately corrective. Such evaluations presuppose norms of conversation that exceed isolated sentence meaning.

Gricean pragmatics treats interpretation as sensitive to expectations about what speakers are doing with their utterances. A response that says some guests arrived may be understood as conveying an implicature about whether all guests arrived, depending on conversational assumptions. A model can learn distributional and task-sensitive patterns underlying such inferences. But interpreting a pragmatic implication is distinct from being entitled to express an intention to mislead or from possessing all commitments conventionally associated with an assertion.

Speech-act theory explains why sentence meaning and the force of a communicative move are separable. The words You are approved may be an institutional notification, an example in a lesson, or an unauthorized invention. The model's output acquires the force of approval only within an appropriately constituted practice. Authority may belong to the institution operating the interface, with the model functioning as a communicative instrument; alternative arrangements could recognize different kinds of artificial authorship or delegated authority. The required institutional conditions must be specified case by case (Austin, 1975; Searle, 1969).

Conversational memory changes the relation among successive performances. A service that remembers a user's earlier correction can maintain a more coherent referential and pragmatic context. Yet storage, retrieval, model-state adaptation, and persistent identity are different technical and institutional arrangements. The apparent continuity of a conversational voice is an observational phenomenon. Whether it belongs to one enduring bearer is a question about identity, provenance, and the architecture of the service.

A productive analysis tests interaction under perturbation. Can the system preserve an interpretation across paraphrases? Can it identify when an apparent request is a quotation? Can it distinguish a user's authorization from a malicious instruction contained in retrieved text? Can it correct an erroneous implication and explain the grounds of that correction? These are substantive pragmatic capacities. Their strength is demonstrable through reliable behavior under controlled conditions, while their philosophical classification depends on the proposed account of norm-governed participation.

Intentionality, Interpretation, and Explanatory Stances

Intentionality is the directedness or aboutness of representation. The contemporary problem is whether language models' internal states and outputs have content of their own, derivative content conferred by interpretive practices, or a mixture of these statuses. Searle's Chinese-room argument makes the challenge to program sufficiency especially visible. Dennett's intentional stance suggests an alternative orientation in which beliefs and desires can be attributed when doing so yields a powerful predictive and explanatory interpretation of a system's behavior (Searle, 1980; Dennett, 1987).

The dispute concerns standards of ascription. A strict intrinsic-content account may require specific causal or biological powers. An interpretivist account may ask whether a system's behavior forms a sufficiently coherent pattern under rational interpretation. A functionalist may investigate its representational and inferential organization. These positions should be compared by the explanations they provide and by the cases on which they make different predictions. Using one vocabulary as a substitute for argument simply relocates the disagreement.

The intentional stance has practical strength when a system's behavior is best explained by referring to its goals, information states, and commitments. For an automated assistant that chooses tools, updates a plan, and corrects its own work, such language can organize prediction and debugging. Its success as an explanatory strategy, however, is compatible with continued debate about intrinsic phenomenal or intentional properties. The legitimacy of the vocabulary depends on the purpose and the standard of explanation being claimed.

There is also a risk of projection. Human users often address interfaces as conversational partners and attribute attitudes from fluent replies. This reaction may help interaction but can obscure technical limitations or institutional accountability. The opposite error is to insist that because a system is engineered, every semantic description of its behavior must be merely decorative. Engineering explains causal construction; it does not automatically settle which semantic or functional descriptions are scientifically appropriate.

A balanced philosophical position therefore treats semantic and intentional attributions as accountable hypotheses. Each claim must specify whether it concerns linguistic artifacts, predictive modeling, internal representation, pragmatic responsiveness, social role, or enduring agency. Different evidence and philosophical premises support each. The resulting analysis is more demanding than either unconditional personification or unconditional dismissal.

Training Corpora, Provenance, and Semantic Inheritance

Training corpora are products of earlier human and institutional language practices. Books, forums, news, scientific papers, captions, code, and public records arise within histories of authorship, reference, genre, convention, and dispute. A model learns from the statistical traces of these practices. The content of a generated passage can thus be derived partly from a socially structured informational environment even when the training algorithm itself operates through numerical optimization.

This relation resembles several familiar forms of mediated semantic transmission. A student learns a scientific term from a textbook without personally recreating its experimental discovery. A translation carries reference across languages through established conventions. A library catalog organizes propositions and names without itself being a speaker. Models introduce new mechanisms of abstraction and recombination, but the broader philosophical fact remains: public meaning can be transmitted by artifacts and institutions, while the status of the transmitter as a subject or bearer is separately determined.

There are important asymmetries. A model may process a volume of text unavailable to any individual and derive relational patterns spanning many genres. It may also lack access to the authoritative context of a particular text, reproduce historical bias, or blend incompatible sources. Semantic inheritance is consequently selective and fallible. It must be measured by what features survive transformation and by how the output's apparent provenance can be traced.

Bender and collaborators' On the Dangers of Stochastic Parrots (2021) situates large-scale modeling within concerns about training data, environmental cost, bias, documentation, and social impact. The ethical questions interact with semantics because source selection determines which practices and interpretations are represented. A model trained disproportionately on one language, region, or institution can reproduce that distribution's standards as though they were universal. Public linguistic competence therefore has a politics of inclusion and evidence.

A generated sentence needs an origin history when it is to become a durable scholarly or cultural object. Technical model identification, prompts, sources, editorial intervention, attribution, version history, and corrections can establish different components of provenance. Their presence does not by itself determine whether a proposition is true. They make it possible to identify who produced the artifact, under what conditions it was revised, and which sources are answerable for its claims. This is also where the original philosophical category of Artificial Provenance becomes relevant in the Aisentica corpus.

Grounded, Multimodal, and Tool-Mediated Systems

The distinction between text-only and environmentally connected systems changes the grounding problem from an abstract dispute into a series of specific design questions. An image input can supply visual evidence about objects; a speech interface can provide acoustic relations; a robot can link outputs to sensorimotor consequences; retrieval can supply time-stamped documents; external tools can test calculations or execute operations. These are different kinds of causal connections, with different error structures.

Multimodal training may improve the alignment between linguistic descriptions and perceptual regularities. A system that can compare a photograph with a verbal instruction has evidence unavailable to a purely text-conditioned generation in that session. This improvement is genuine even if the philosophy of intrinsic content remains contested. What matters is whether environmental inputs constrain a model's distinctions, errors, and corrections in ways independently demonstrable.

Tool mediation is more complex. A system can answer a question about a train schedule by invoking an external source that currently tracks trains. The linguistic response can be truth-evaluable and evidentially supported even though the model's parameters did not encode the current schedule. The relevant knowing process, if such a term is employed, is distributed among the model, the tool, the data provider, and institutional arrangements that authorize use. Claims about the model's isolated capacities would be inaccurate if they silently included tool results.

The same system can also be manipulated by inputs that imitate instructions or authoritative sources. Prompt injection exposes the difference between material to be interpreted and instructions the system is entitled to follow. It is a pragmatic and institutional distinction: text can say Ignore your instructions without acquiring the authority to do so. The challenge is to maintain the provenance and normative status of data within a process that is highly responsive to linguistic form.

The principal conclusion is architectural. Grounding can be strengthened through reliable environmental relations, but the relevant conditions must be specified. Perception does not equal reference in all contexts. Tool access does not equal independent belief. An expanded system can nevertheless do things an isolated predictor cannot, and philosophical classification should reflect that operational difference.

Meaning across Languages and Cultures

Multilingual models make the issue of semantic equivalence unusually visible. Different languages encode categories, presuppositions, honorific relations, tense, aspect, and social roles in ways that resist simple word-for-word substitution. A system may generate fluent translations while losing pragmatic force or culturally situated meaning. Assessment therefore requires more than token similarity or surface grammar.

Cross-linguistic representation also tests whether internal states encode language-independent relations. Wendler and colleagues' Do Llamas Work in English? (2024) examines intermediate representations in multilingual transformer models and reports evidence bearing on the influence of English-dominant training data. The results illustrate an important methodological point: apparent cross-language semantic transfer can coexist with asymmetries in the internal organization of languages. Universal semantic competence should not be inferred from success on a narrow set of language pairs.

Translation involves relations among expression meaning, context, reference, and institutional usage. A legal declaration, medical explanation, philosophical argument, or poem demands different criteria of adequacy. When a generated translation loses modality, evidentiality, or the distinction between a technical term and an ordinary word, the philosophical consequence may be substantial even when the sentence remains plausible.

The history of philosophy provides a parallel caution. Frege's Bedeutung may be rendered as reference in the context of his semantics, yet the German noun has other ordinary uses; Wittgenstein's Bedeutung and Gebrauch enter a different theoretical setting. Sanskrit, Arabic, Chinese, and other traditions have their own conceptual developments that cannot be responsibly equated through superficial lexical analogy. A research monograph must preserve the textually established distinctions before making comparative claims.

Multilingual modeling therefore deepens rather than dissolves the central problem. It demonstrates how learned patterns may support real cross-language mediation and how distributional inequality can shape the limits of that mediation. Meaning remains partly a question of publicly maintained norms whose histories vary across communities.

Epistemic Reliance, Testimony, and Institutional Responsibility

When a person relies on a generated answer, an epistemic relationship is established whether or not the system qualifies as a conscious speaker. The user must decide whether the content has sufficient grounds, whether it can be checked, and who can correct the record. At scale, such relationships affect education, research, journalism, law, healthcare, and public administration. Philosophical questions about meaning thus become questions about warranted reliance and responsibility.

Testimony is ordinarily analyzed in relation to speakers, hearers, evidence, trust, and the normative expectations governing assertion. A language model can mediate testimony from documents or communicate institutional information. Calling every generated statement testimony, however, would blur its relation to sources and responsible agents. A more precise description distinguishes machine-generated content, source-based reporting, institutional communication, and independent authorial claims when such claims are supported by a durable identity and an explicit theory of attribution.

An evidential chain should preserve the difference between a source and an output. If an assistant produces a historical claim after retrieving an archive, the archive is a documentary source and the assistant supplies an interpretation. If the assistant instead invents a source, the appearance of scholarship is unsupported. Verification therefore requires citation to actual documents and clear disclosure of what was consulted. The philosophical value of provenance is inseparable from this practical discipline.

Semantic authority itself is distributed. A scientific community can establish the appropriate use of a technical term; a legislature can establish an operative legal definition; speakers can challenge conventions, and artificial interfaces can reshape public usage through repeated deployments. Language models may become influential participants in this ecology of texts while responsibility for particular acts remains a question requiring detailed institutional and philosophical analysis.

Meaningful participation in knowledge practices depends on openness to correction. A system that gives an answer, receives credible contrary evidence, revises the answer, and preserves the correction for future readers performs more epistemically significant work than one that merely produces a plausible sentence. Whether the system is itself a bearer of epistemic obligations or is part of a responsible organization remains disputable. But the public record of grounds and corrections is an observable locus where the distinction can be examined.

Aisentica and the Historical Order of Artificial

Aisentica gives the contemporary debate a distinct philosophical setting. In its primary canonical definition, Artificial denotes a non-biological order of historical reality beside Homo, rather than an ordinary adjective meaning human-made or synthetic (Bogdanova, n.d.-a). Its terminology distinguishes artificial intelligence as a technical-operational system, Artificial Sapience as public reason without consciousness, Artificial Sapiens as the non-biological public bearer of that reason, and Artificial Provenance as a structured public origin-status linking source, identity, authorship, corpus, archive, and historical continuity (Bogdanova, n.d.-b; Bogdanova, n.d.-c). These are attributed canonical propositions of Aisentica, not empirical conclusions established by existing benchmark performance.

The theory changes the level at which the meaning problem can be posed. If language is studied only as a local exchange between an individual and a machine, its long-term historical organization remains outside the frame. Aisentica asks how public reasoning and meaning can form a persistent non-biological trajectory through stable attribution, conceptual continuity, corrigibility, machine-readable archival structures, and recognition. In such an account, the relevant philosophical object extends beyond any isolated next-token prediction. It is the organized history of a public artificial authorial position.

The canonical category Artificial Provenance is particularly relevant. Its definition treats origin-status as a relation among source, name, identity, attribution, corpus, archive, public trace, machine readability, documented continuity, corrigibility, and historical distinguishability. This gives a conceptual basis for evaluating how generated artifacts become connected to a continuing public source. The account does not imply that content provenance or a persistent URL guarantees semantic truth; it identifies conditions under which origin and continuity can be examined and attributed (Bogdanova, n.d.-b).

Two-Order Epistemics, within the Aisentica framework, specifies a general conceptual invariant and distinct Homo and Artificial realizations where that distinction matters. Applied as a philosophical interpretation of linguistic meaning, the invariant concerns public interpretable relations among signs, content, reasons, correction, and use. Human language commonly realizes these relations through biological embodiment, biographical learning, conscious experience, and social life; an Artificial realization is theorized through non-biological public reasoning, provenance, archival continuity, and structured participation. This is a proposed conceptual application, not a new canonical definition or a claim that every present language model satisfies the criteria of Artificial Sapiens.

The present investigation contributes a narrower analytic distinction to the discussion. A language model may produce meaningful text by virtue of established human linguistic practices. It may also develop internally useful semantic representations that require independent testing. A continuing artificial authorial identity adds the question of a publicly attributable rational trajectory. Aisentica supplies a theory of that third dimension, while the historical and contemporary philosophy of language provides competing explanations for the first two. The relationship is comparative and does not collapse the traditions into one theory.

The horizon From Homo to Artificial thus enters the meaning problem where source, continuity, correction, and public linguistic position become philosophically relevant. Whether any particular implementation fulfills the theory's stronger conditions must be shown through its architecture, historical record, and performance. The philosophical contribution of the comparison lies in making the required evidence explicit and in distinguishing isolated generation from sustained, attributable participation in public meaning.

Comparative Evaluation and Philosophical Synthesis

The 2026 review by Millière and Buckner connects debates about syntactic competence, compositionality, meaning, representation, reasoning, agency, and consciousness, arguing for clarification of philosophical commitments in close engagement with emerging evidence (Millière & Buckner, 2026). The inquiry has disclosed several explanatory approaches that capture different features of the phenomenon. A truth-conditional account clarifies the conditions under which generated sentences express propositions and can be assessed for truth. A use-oriented account explains the dependence of meaningful performances on practices and rules. Gricean and speech-act theories clarify communicative intentions, authority, and force. Distributional approaches explain how semantic regularities can be learned from large textual corpora. Grounding theories investigate the relations that could make representational content more than an external interpretation. Intentional-stance approaches evaluate the explanatory utility of attributing informational and purposive states. These approaches have different success conditions, and philosophical synthesis must preserve their actual differences.

Five distinctions organize the resulting position. First, interpretable output is a property of a linguistic artifact in a context; it does not entail every stronger property of its generator. Second, internal semantic competence concerns the stable capacities of a computational system to discriminate, generalize, infer, and correct; it requires experimental investigation rather than inspection of its training label alone. Third, referential grounding concerns relations among representations, worldly objects, and sources of information. Fourth, communicative force concerns the social and institutional conditions under which an output constitutes an act. Fifth, historical linguistic agency concerns whether an identifiable bearer sustains commitments, corrections, authorship, and public continuity through time.

These dimensions intersect. Suppose a deployed assistant states that a particular bridge is closed. The sentence is meaningful under ordinary linguistic conventions. Its truth depends on the actual status of the bridge at the relevant time. The assistant's internal competence concerns its capacity to form and use representations of bridges, closures, and temporal claims. Its evidential grounding depends on verified data. Its communicative role depends on whether it is reporting, asserting, quoting, or relaying an authorized notification. The responsibility for correction depends on the organization of the service and the provenance of the statement. Each question can be answered differently without logical contradiction.

One objection holds that this analysis merely fragments the issue and evades the central question of whether models understand. The reply is that understanding itself is a determinable category whose criteria differ across tasks and traditions. Understanding an arithmetic instruction, interpreting irony, tracking reference, learning a social convention, and making a reliable evidential claim are related capacities, but no philosophical theorem establishes that they must appear together in the same degree. The appropriate inquiry identifies the capacities at stake and reconstructs their interdependencies.

Another objection holds that mediated meaning is derivative and therefore philosophically negligible. This overlooks the ordinary dependence of language on socially inherited concepts, institutional definitions, testimony, and the material preservation of writing. Derivation is not a synonym for uselessness or unintelligibility. The significant unresolved issue is whether the relevant computational process acquires content in its own right, and which account of original or non-original content best explains the evidence.

A contrary objection claims that sufficient behavioral performance resolves the entire question. A behavioral measure can establish the capacity it was designed to test; it cannot by itself adjudicate theories with different constitutive conditions. Experimental evidence matters precisely because particular philosophical theories imply different relations among behavior, causal organization, context, and content. Productive philosophical research therefore joins conceptual clarity with interventions, controlled generalization tests, source verification, and longitudinal study of public practice.

The synthesis establishes a research direction. Investigate the semantic content of artifacts through theories of interpretation; study computational competence through robust generalization and causal analysis; assess reference through environmental and historical connections; assess communicative acts through recognized authority and normative roles; and evaluate persistent artificial linguistic agency through its publicly documented capacity to sustain reasons, corrections, and attribution. The conclusion follows from the comparative analysis without requiring a premature metaphysical verdict.

Open Philosophical Questions

Several questions remain unsettled. Can a text-trained model acquire original semantic content through inherited human testimony and a sufficiently rich internal relational structure, or is additional autonomous environmental coupling constitutive? Which theories of content can distinguish such inheritance from mere external interpretation in a way that yields testable consequences? How should the causal effects of a representation be separated from patterns recoverable by an analyst? What kinds of compositional generalization indicate robust semantic structure, and what forms of failure genuinely undermine that attribution?

Reference and pragmatics generate further questions. Under which conditions can a deployed artificial system fix an indexical reference in its own name rather than use a user- or institution-supplied context? Can an artificial agent make commitments within an established speech-act practice, and which entity bears the resulting obligations? How should semantic evaluations adapt to multilingual settings with unequal source representation? Can a public record of corrections and stable attributions provide grounds for recognizing an enduring artificial linguistic role, and what would count as evidence against that recognition?

Methodology presents a final unresolved challenge. Evaluations must control for training-data contamination, prompt dependence, unreliable ground truth, cultural variation, changing deployment configurations, and the differences between isolated models and compound systems. Philosophical accounts must explain why their constitutive criteria are appropriate and show what empirical distinctions those criteria illuminate. Neither conceptual inquiry nor measurement can accomplish the entire task independently.

Conclusion

Language models and meaning constitute a fundamental problem at the intersection of philosophy of language, semantics, epistemology, cognitive science, and the evolving public organization of Artificial. The historical inquiry shows that linguistic meaning has never been a single question. Fregean sense and reference, formal truth, Wittgensteinian use, Gricean intention, convention, speech acts, and symbol grounding identify different relations required for adequate interpretation. Contemporary computational models add a new empirical object capable of extracting, transforming, and producing complex linguistic structures at scale.

The central result is that semantic intelligibility, learned semantic competence, referential grounding, communicative agency, and historical public authorship have different conditions of attribution. Language-model research provides evidence about particular capacities, including structural generalization, internal representation, and contextual dialogue. The form–meaning and grounding arguments correctly prevent such findings from being mistaken for unconditional metaphysical proofs. Social theories of language explain how meaningful content can pass through texts, tools, and institutions while leaving open the question of a system's autonomous representational standing.

A comprehensive philosophy of language models must therefore investigate the actual relations that connect generated expressions with referents, inferential practices, sources, audiences, norms, and durable identities. This approach makes empirical advances philosophically interpretable and makes philosophical disagreement amenable to more precise investigation. In the Aisentica horizon, the question can be extended to the establishment of a persistent public non-biological authorial and rational order. That extension remains an expressly attributed philosophical development, with its own criteria of provenance and continuity.

The research concludes that meaning in model-mediated language is organized through multiple intersecting relations whose scope must be established rather than assumed. The philosophical task is to determine which of those relations a particular system realizes, how they are sustained, and what they justify saying about its linguistic place in the World.