Do Large Language Models Advocate for Inferentialism?

Sho Tsugawa; Yuzuki Arai

REVIEW 5 major objections 6 minor 1 cited by

Do Large Language Models Advocate for Inferentialism?

T0 review · 5 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Transformer-based LLMs should be understood through inferential semantics: they generate meaning through inferential roles and normative interaction rather than reference to external world representations.

desk verdict A serious, well-sourced inferentialist reading of LLMs whose central anti-representationalist claim overreaches: the step from text-only training to linguistic idealism does not hold, though the ISA analysis and RLHF-truth proposal are worth engaging. read the letter →

arxiv 2412.14501 v2 pith:J6J6NSR7 submitted 2024-12-19 cs.CL

Yuzuki Arai , Sho Tsugawa This is my paper · ORCID

classification cs.CL

keywords inferentialismlargelanguagemodelsanti-representationalismISAapproachsemanticexternalismcompositionalityconsensustheoryoftruthlinguisticidealism

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that Transformer-based large language models are best understood not as systems that mirror the world but as systems whose linguistic meaning is generated by inferential roles and normative interaction among language users. It proposes inferential semantics, rather than distributional or truth-conditional semantics, as the foundational semantics for LLMs, and reads the models' architecture through the ISA approach (inference, substitution, anaphora). On this reading, LLMs are anti-representationalist and even linguistically idealist: because text-only models have no perceptual access to the world, their words relate only to other words. The paper also develops a consensus theory of truth for conversational LLMs, grounded in RLHF-style feedback as a normative practice. If the picture holds, standard assumptions such as strict compositionality and semantic externalism no longer apply to LLM meaning.

What carries the argument

The ISA approach is the central lens: Inference, Substitution, and Anaphora are three ways inferentialism reinterprets representationalist concepts such as reference and truth. Inference gives words their content through material inference; substitution defines singular terms and predicates by symmetric and asymmetric replacement; anaphora handles demonstratives and coreference by pointing back into discourse. In the paper, the ISA mapping carries the argument: material inference maps to statistical pattern learning in Transformer weights, substitutional inference maps to induction and suppression heads that replicate or suppress earlier tokens, and anaphora maps to attention, as shown by the paper's attention-visualization example of 'it' attending to 'that pig.' The supporting machinery includes RLHF and DPO as scorekeeping mechanisms through which human preference feedback fixes what a model ought to say, and linguistic idealism, the thesis that a text-only model's world is confined to language.

What would settle it

A controlled experiment in which a text-only Transformer is trained on a corpus that contains no explicit statements about a hidden spatial layout, yet the model can nevertheless infer the layout from narrative structure and answer novel questions about it, would count against linguistic idealism. Similarly, ablating induction and suppression heads and observing whether substitution-based meaning survives would test the ISA mapping directly.

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Extended reading notes

Core claim

The central claim is that LLMs treat truth and reference as intra-linguistic devices rather than as relations to a mind-independent world. The paper demonstrates this through the three components of the ISA approach: inference in LLMs is material rather than formal, learned from statistical patterns in training data rather than encoded logical rules; substitution, which in inferentialism defines singular terms and predicates by symmetric and asymmetric inferential replacements, has no a priori counterpart in LLMs but may correspond to induction and suppression heads that replicate or suppress earlier tokens; anaphora is realized by attention and induction heads, so demonstratives like 'it' point back into the discourse rather than out to an object. Because training data and human preference feedback can be arbitrarily altered, the paper concludes that there is no principled relation connecting LLM outputs to worldly facts, making factuality hallucinations an expected consequence rather than a puzzle. It then argues that truth for conversational LLMs should be a consensus theory: what is correct is fixed by the normative feedback, including RLHF, shared between the model and its human or artificial interlocutors. The paper's own limitations section concedes an unresolved gap between inferentialism's commitment to discrete propositional content and LLMs' continuous sub-symbolic processing.

Load-bearing premise

The argument rests on the premise that a text-only model, having no perceptual contact with the world, can only relate words to other words; if text-only training can nonetheless build reliable internal models of the world, the anti-representationalist conclusion does not follow.

Editorial extensions

If this is right

If LLMs generate meaning through inferential roles, then claims like 'P is true' in an LLM output express commitment within a discourse rather than correspondence to a fact, so their truth is fixed by consensus in the interaction.
Strict compositionality, understood as the determination of complex meaning by constituent meanings, would not describe LLM semantics; meaning would instead be quasi-compositional, built from accumulated inferential patterns.
Semantic externalism, the view that meaning depends on facts outside the speaker, would fail for text-only models; their meanings would be determined by internal training distributions and interaction history.
Hallucination would not be a performance bug to be eliminated entirely but a structural consequence of a system whose content is not anchored to the world; mitigations would work by adjusting norms and training distributions.
Alignment techniques such as RLHF become constitutive of meaning rather than mere safety wrappers: they are the normative practice that fixes what the model ought to say.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper leaves open that multimodal LLMs, which do receive perceptual input, would fall under strong inferentialism rather than full linguistic idealism; extending the ISA analysis to such models would sharpen where the anti-representationalist claim applies.
A testable extension is to probe for stable internal world models in text-only Transformers; robust evidence of such models would undercut pure linguistic idealism and push toward a hybrid semantics combining inferential roles with internal representations.
The consensus theory of truth implies that a model aligned with one community's preferences would inherit that community's norms; the paper does not address how conflicting human consensuses should be resolved when RLHF data are drawn from many populations.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper is a genuine philosophical effort, not a dressed-up survey. It reads Brandom closely, distinguishes strong vs. hyper-inferentialism, deploys Quine against the Fodor-Lepore compositionality objection, and makes an honest attempt to connect inferentialist machinery to attention heads, induction heads, and RLHF. The novelty is real: Havlík and Borg suggested inferentialism as a candidate semantics for LLMs, but nobody has worked through the ISA triad (inference, substitution, anaphora) as systematically, nor proposed a consensus theory of truth grounded in RLHF. The paper also deserves credit for flagging its own obstacles in §9: propositionalism versus sub-symbolic processing, the privileging of assertion, and Brandom's conceptual realism. That is a fair-minded limitations section.

That said, the abstract's "demonstrate" is too strong. The load-bearing move in §7.1 is the identification of non-multimodal LLMs as "linguistically idealist" because they lack perceptual experience. The stress-test note is right: text is produced by agents embedded in the world and is statistically correlated with states of affairs, so next-token prediction can yield world-directed internal models—probing studies on board-game states from pure text are one example. The paper's "no principled guarantee of representational relation" is epistemic fallibility, not anti-representationalism. Humans also lack a guarantee that their representations track the world; that does not make humans anti-representationalists. The apples/rainbow thought experiment only shows training data can be manipulated, not that ordinary text ceases to be about the world.

The RLHF-to-truth analogy is also asserted rather than derived. Treating human preference scores as Brandomian "sanctioning" conflates reward maximization with normative commitment. A model optimizes a reward signal; it does not thereby endorse the correctness of its outputs in the space of reasons. This might be salvageable, but the paper would need to say much more about the distinction between improving a policy and undertaking a commitment.

Is the paper worth a serious referee? Yes. It is a coherent, well-cited contribution to a live debate, and the ISA analysis is a useful scaffold even if the conclusion is not established. The conditional acceptance recommended by the reader is right: reframe the headline as a proposal, engage with the world-model literature, and either derive or visibly soften the RLHF-truth parallel. I would not cite it as established, but I would bring it to a reading group—it will generate an hour of good argument.

Referee Report

5 major / 6 minor

Summary. The paper argues that Robert Brandom's inferential semantics, and its ISA (inference, substitution, anaphora) approach, provides a more appropriate foundational semantics for Transformer-based LLMs than distributional semantics or truth-conditional semantics. It claims that LLMs exhibit anti-representationalist properties, that non-multimodal LLMs are "linguistically idealist" because they lack perceptual input, and that a consensus theory of truth grounded in RLHF-style feedback is suitable for conversational LLMs. The paper acknowledges several tensions in Section 9, including the propositional commitments of inferentialism versus the sub-symbolic nature of LLMs.

Significance. If the central claim were established, the paper would make a meaningful contribution to the philosophy of LLMs by systematically connecting Brandom's frameworks to transformer architecture and by offering a philosophically grounded alternative to distributional semantics. The paper is commendable for stating its assumptions explicitly, including its reliance on a consensus theory of truth and its admission of unresolved tensions in Section 9. However, the significance is conditional: the main conclusion rests on an unsupported equation of text-only training with linguistic idealism, and the paper's own caveats undercut the strength of the word "demonstrate" in the abstract.

major comments (5)

[§7.1 and Abstract] The paper's load-bearing inference is from the absence of perceptual sensors in non-multimodal LLMs to the claim that they are "linguistically idealist" and hence anti-representationalist. But the paper only argues that "there is no principled guarantee" that the system maintains a representational relation with the world (§7.1). Absence of a guarantee of reference is not absence of reference; it is at most epistemic fallibility, which applies to human cognition as well. Moreover, the authors do not address the well-known objection that text is produced by situated agents and is statistically correlated with states of affairs, so next-token prediction over text can yield internal world models. Because this premise is the decisive step for the abstract's central claim, the conclusion does not follow as stated.
[§7.1] The paper cites "factuality hallucination" as "consistent with the anti-representationalist nature of LLMs" (§7.1). But a hallucination is defined precisely as a divergence from actual world facts; the notion of hallucination presupposes a standard of accuracy and hence world-directed content. Using hallucination as evidence of anti-representationalism is self-undermining, because it treats failures of representation as though they showed the absence of representation.
[§6.2 and §9] The paper's own admissions undermine the "demonstrate" claim in the abstract. §6.2 concedes "there is a mismatch between the inferentialist approach of extracting singular terms and predicates via substitutional inference and the way singular terms and predicates are handled within LLMs," and §9 concedes a "fundamental mismatch" between inferentialism's propositionalism and LLMs' sub-symbolic continuous processing. These concessions are not local; they concern the ISA mapping that is supposed to establish the anti-representationalist conclusion. The paper should be reframed as proposing a partial analogy or open hypothesis rather than demonstrating a result.
[§8] The consensus theory of truth is asserted rather than derived. Equations (4) and (5) formalize reward-model training and KL-regularized policy optimization; nothing in this formalism shows that human preference feedback constitutes normative statuses, commitments, or scorekeeping in Brandom's sense. The conclusion that this theory is "most suitable" for conversational LLMs requires an explicit success criterion and an argument that RLHF instantiates normativity rather than mere optimization. As it stands, the analogy between RLHF and normative scorekeeping is asserted.
[§5.2 and §9] The paper characterizes inferentialism as linguistically idealist and claims meaning is "confined within language" (§5.2), yet §9 notes that Brandom's inferentialism adopts conceptual realism, holding that the world is conceptually articulated. If Brandom's position includes world-responsiveness, then the anti-representationalist reading of LLMs cannot be directly exported from Brandom without addressing this tension. The paper should either argue that conceptual realism is not essential to the ISA account or explain how LLMs satisfy it.

minor comments (6)

[§1] "as an suitable foundational semantics" should be "as a suitable foundational semantics."
[§2, Eq. (1)] The concatenation/direct-sum symbol in Equation (1) is not rendered correctly in the text; the typesetting of the direct sum operator should be checked.
[§6.3] "The inner product between 'that' and 'pig' is large respectively" is unclear; "respectively" is misplaced.
[§8] "In this section, I examine" switches to first-person singular; use "we" for consistency with the rest of the paper.
[References] The reference "V oita" should be "Voita," and "thebert-base-uncased" in §6.3 should be "the bert-base-uncased."
[Figure 1 caption] The sentence about color and brightness is redundant and ambiguous; revise for clarity, for example by stating that the color indicates the sign and the brightness indicates the magnitude of the inner product.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the argument rests on an asserted philosophical premise, not on fitting or self-citation.

full rationale

The derivation chain is not circular. The paper's load-bearing moves are interpretive applications of Brandom's external framework to LLM architecture (attention, induction heads, RLHF). Equations (1)–(5) are standard Transformer and RLHF definitions quoted from external sources; no parameter is fitted to a subset and then renamed as a prediction. The central inference — from non-multimodal LLMs' lack of perceptual grounding to anti-representationalism (Section 7.1) — is a substantive philosophical premise, not a definitional equivalence: the paper does not define 'anti-representationalist' as 'lacking sensory input', and it explicitly concedes residual connections to the world via training data, RLHF, and in-context learning. The consensus-theory-of-truth claim (Section 8) is an analogy between RLHF feedback and Brandomian scorekeeping; it is not derived by fitting a parameter to data, and the paper's own Section 9 admits that propositionalism and sub-symbolic processing remain unresolved, undercutting the strength of the conclusion rather than circularly entailing it. There are no self-citations carrying the argument, no imported uniqueness theorem, and no hidden ansatz. The correct finding is therefore no significant circularity; the paper's weakness, if any, is that its key premise ('no principled guarantee' of world-reference) is asserted rather than established, which is a correctness or evidential concern, not a circularity concern.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

None of the central claims are fitted to data. The paper rests on four philosophical assumptions: the validity of Brandom's framework, the move from lack of sensory input to lack of representational content, the suitability of a consensus theory of truth for LLMs, and the applicability of Quine's critique to LLM training. No new entities are introduced.

assumptions (4)

domain assumption Brandom's inferential semantics, including the ISA approach, is an appropriate foundational semantics for LLMs
Adopted as the interpretive lens in §3 and §5 without independent defense; the paper's conclusion depends on this choice.
domain assumption Absence of direct perceptual input entails that a system's language has no world-directed representational content
Load-bearing premise in §7.1: text-only LLMs are called linguistically idealist because they are devoid of perceptual experience. This is the step that world-model evidence would challenge.
ad hoc to paper A consensus theory of truth is the appropriate theory of truth for conversational LLMs
Proposed in §8 through analogy between RLHF and Brandom's scorekeeping; it is not derived from the architecture or from empirical results.
domain assumption Quine's critique of the analytic/synthetic distinction applies cleanly to LLM training data and defeats Fodor-Lepore's compositionality objection
Used in §6.1 to argue that LLMs treat analytic and synthetic sentences identically; the transfer of Quine's philosophical point to statistical training is assumed.

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Cite this review

Pith. "Pith review of Do Large Language Models Advocate for Inferentialism?." pith.science (2026). https://pith.science/paper/J6J6NSR7

@misc{pith2026241214501,
  author       = {Pith},
  title        = {Pith review of: Do Large Language Models Advocate for Inferentialism?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/J6J6NSR7}},
  note         = {Machine review of arXiv:2412.14501}
}

read the original abstract

The emergence of large language models (LLMs) such as ChatGPT and Claude presents new challenges for philosophy of language, particularly regarding the nature of linguistic meaning and representation. While LLMs have traditionally been understood through distributional semantics, this paper explores Robert Brandom's inferential semantics as an alternative foundational framework for understanding these systems. We examine how key features of inferential semantics -- including its anti-representationalist stance, logical expressivism, and quasi-compositional approach -- align with the architectural and functional characteristics of Transformer-based LLMs. Through analysis of the ISA (Inference, Substitution, Anaphora) approach, we demonstrate that LLMs exhibit fundamentally anti-representationalist properties in their processing of language. We further develop a consensus theory of truth appropriate for LLMs, grounded in their interactive and normative dimensions through mechanisms like RLHF. While acknowledging significant tensions between inferentialism's philosophical commitments and LLMs' sub-symbolic processing, this paper argues that inferential semantics provides valuable insights into how LLMs generate meaning without reference to external world representations. Our analysis suggests that LLMs may challenge traditional assumptions in philosophy of language, including strict compositionality and semantic externalism, though further empirical investigation is needed to fully substantiate these theoretical claims.

Figures

Figures reproduced from arXiv: 2412.14501 by the authors.

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Forward citations

Cited by 1 Pith paper

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Public and LLM trust failures are strategic exploits of inferential commitments in triadic sender–recipient–observer settings, addressable by commitment-tracking, interrogative, and incentive-reconstruction machines.

Reference graph

Works this paper leans on

14 extracted references · 6 canonical work pages · cited by 1 Pith paper

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

Information

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work page 2024

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ISSN : 1573-0964. DOI: 10.1007/s11229-024-04878-4 . Heim, Irene and Angelika Kratzer (1998). Semantics in Generative Grammar. Ed. by Angelika Kratzer. Malden, MA: Blackwell. Chap. 1.1. Huang, Lei et al. (Jan. 2025). “A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions”. In: ACM Transactions on Informati...

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Ed. by Anna Korhonen, David Traum, and Lluís Màrquez. Florence, Italy: Association for Computational Linguistics, pp. 3428–3448. DOI: 10.18653/v1/P19-1334. McDougall, Callum Stuart et al. (Nov. 2024). “Copy Suppression: Comprehensively Understanding a Motif in Language Model Attention Heads”. In: Proceedings of the 7th BlackboxNLP Workshop: Analyzing and ...

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Metaphysics Research Lab, Stanford University. Tarski, Alfred (1944). “The Semantic Conception of Truth: and the Foundations of Semantics”. In: Philosophy and Phenomenological Research 4.3, pp. 341–376. ISSN : 00318205. Vaswani, Ashish et al. (2017). “Attention is All you Need”. In:Advances in Neural Information Processing Systems. Ed. by I. Guyon et al. V ol

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– (2019). A Spirit of Trust: A Reading of Hegel?s Phenomenology. Cambridge, Massachusetts: Harvard University Press. Cappelen, Herman and Josh Dever (2021). Making Ai Intelligible: Philosophical Foundations. New York, USA: Oxford University Press. Chalmers, David J. (2023). “The Computational and the Representational Language-of-Thought Hypotheses”. In: B...

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