On the Compatibility of Generative AI and Generative Linguistics

Eva Portelance; Masoud Jasbi

REVIEW 3 major objections 5 minor 89 references

On the Compatibility of Generative AI and Generative Linguistics

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper argues that neural language models are not a break from Chomsky's generative linguistics but a continuation of it, because they are formal generative models, can serve as discovery procedures, and can advance the Minimalist…

desk verdict A well-organized position paper whose three-part compatibility argument is undercut by an unvalidated 'derivational likelihood' construct—absent for transformers and untested for grammar induction models. read the letter →

arxiv 2411.10533 v2 pith:WBFVYOOO submitted 2024-11-15 cs.CL

Eva Portelance , Masoud Jasbi This is my paper · ORCID

classification cs.CL

keywords languagemodelsgenerativelinguisticsChomskyhierarchydiscoveryproceduresgrammarinductionderivationallikelihoodMinimalistProgramacquisition

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 the current wave of neural language models is not a break from Chomsky's generative linguistics but a continuation of it. The authors claim that LMs are formal generative models in Chomsky's original sense, that they can serve as discovery procedures which induce grammars from data, and that they can support the Minimalist Program's search for what must be innate in language. If the argument holds, the common opposition between statistical language models and rule-based generative grammar dissolves, and each side becomes a tool for the other. The argument turns on the claim that some LMs track derivational likelihood—a model-internal probability of a sentence given latent grammatical structure—and not just surface string frequency.

What carries the argument

The central object is derivational likelihood: the probability that a string is derived given the model's full latent state and weights, as opposed to string likelihood, which is computed by sequential Markovian sampling from the final layer. The distinction lets the authors answer the charge that LMs are only observationally adequate—the paper argues the problem is not that derivational likelihood does not exist but that it is not always easy to extract. The surrounding machinery is Chomsky's hierarchy of formal languages, the three levels of adequacy, and the discovery/decision/evaluation procedure trichotomy, which together locate LMs within generative linguistics rather than outside it.

What would settle it

Train a grammar induction language model on a corpus and test it on the pair 'Colorless green ideas sleep furiously' versus 'Furiously sleep ideas green colorless.' If the model's derivational likelihood ranks the grammatical sentence above the ungrammatical one while its string likelihood does not, the distinction is real; if no accessible internal quantity separates them, the discovery-procedure claim collapses.

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Extended reading notes

Core claim

The paper's central claim is that LMs are formal generative models as Chomsky originally defined them: devices that generate a set of sentences and are evaluated by observational, descriptive, and explanatory adequacy. Against the objection that LMs only provide string likelihood and therefore cannot distinguish grammaticality from probability, the paper asserts that LMs can in principle learn grammars and some decidedly do—specifically grammar induction LMs, which learn distributions over explicit syntactic rules and make derivational likelihood directly accessible. This makes LMs usable as discovery procedures of the kind Syntactic Structures described, and as computational implementations of the Minimalist Program's third-factor methodology, where whatever cannot be learned from data plus general learning principles is a candidate for innate endowment.

Load-bearing premise

The argument stands or falls on the premise that language models contain an internal score for how a sentence is derived grammatically, distinct from how likely the surface string is—a score the paper concedes we often cannot currently extract.

Editorial extensions

If this is right

LMs can be used as discovery procedures that induce grammatical categories and rules directly from corpora, without a pre-specified grammar.
Grammar induction LMs can simulate language acquisition and be checked against children's developmental milestones, giving explanatory adequacy an empirical handle.
LMs' systematic failures under data-driven and third-factor assumptions become hypotheses about which aspects of language are innate.
Chomsky's levels of adequacy provide a scientific evaluation standard for LMs distinct from engineering metrics.
Multimodal versions of these models may bring linguistics closer to the full multimodal experience of human language learning.

Reading between the lines

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

If derivational likelihood is real but only readily accessible in grammar induction LMs, the strongest empirical case for the paper rests on that niche class; a natural extension is to probe transformer hidden states for a recoverable analogue.
The paper's framework predicts that adding multimodal grounding and child-scale data to grammar induction LMs should narrow the explanatory-adequacy gap with human learners—a concrete direction for testing the Minimalist claim.
The compatibility argument also recasts benchmark evaluation: if descriptive adequacy is the target, tests of structural ambiguity like 'pancakes or bacon and eggs' become more diagnostic than perplexity or accuracy scores.

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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

This paper is a genuinely useful synthesis of the current debate, but the bridge it relies on is not built. The three-part argument—LMs as formal generative models, LMs as discovery procedures, LMs as Minimalist Program assets—is clearly organized and historically careful. The discovery-procedure framing is the freshest part: it gives grammar induction LMs a concrete scientific role that the "LMs are just tools" positions don't. The distinction between string likelihood and derivational likelihood is a helpful way to organize why n-gram style objections don't automatically apply to neural models. The authors also deserve credit for flagging that extraction from black-box LMs is currently impossible.

The soft spot is exactly where the stress-test note lands. Section 3 asserts that LMs "can in principle learn grammars and some decidedly do so" because they can support a derivational likelihood distinct from string probability. For transformer LMs, that quantity is not defined—there is no latent derivation variable in an autoregressive transformer, so "derivational likelihood given all relevant weights and states" has no formal content separate from the model's string probability. The paper says the value exists but we can't extract it; the deeper problem is that it's not clear what the value is. For grammar induction LMs, derivational likelihood is well-defined relative to an induced grammar, but nothing in the paper shows that this quantity tracks grammaticality. A PCFG induced from a corpus can assign low probability to rare grammatical sentences and high probability to ungrammatical ones; grammaticality is not a threshold on one model's derivation score. So the compatibility thesis rests on an unvalidated construct: absent for the models that motivate the debate, unvalidated for the subclass where it's defined.

The reliance on Portelance et al. (2024) for representativeness is a real weakness, though self-citation alone isn't disqualifying. The "cognitive duck test" is a framing device, not evidence, which is fine—but it shouldn't be read as doing more than labeling.

For a position paper, this is solid. The argument is coherent, the literature engagement is fair, and the authors are explicit about key limits. It's not a demonstrated result, and the title overpromises: compatibility is conditional on a derivational-likelihood notion that may not survive contact with actual model internals. Who gets value: anyone in the Piantadosi/Kodner/Katzir debate, and anyone thinking about whether grammar induction models can carry weight in learnability research. It deserves a serious referee—the revision should either define derivational likelihood for a concrete model class or narrow the claim to grammar induction LMs and validate it against grammaticality judgments. I'd take it for reading group.

Referee Report

3 major / 5 minor

Summary. This conceptual position paper argues that neural language models are compatible with, and indeed reinforce, Chomskyan generative linguistics in three ways: (1) LMs are formal generative models in the sense of Chomsky's formal language theory; (2) LMs can serve as discovery procedures in the sense of Syntactic Structures; and (3) LMs can contribute to the Minimalist Program's 'bottom-up' identification of what must be attributed to genetic endowment. The central sub-claim, developed in Section 3, is that LMs can in principle learn grammars and that some 'decidedly do so', thereby distinguishing string likelihood from derivational likelihood, which is equated with grammaticality. The paper is a position piece and contains no new experiments, derivations, or code.

Significance. If the central sub-claim could be made precise and empirically supported, the paper would provide a genuinely useful bridge between computational linguistics and generative linguistics. It is strongest where it connects Chomsky's levels of adequacy to the evaluation of LMs, and where it draws attention to grammar induction models as transparent, underused discovery procedures. The paper does not ship machine-checked proofs, reproducible code, or falsifiable quantitative predictions, so its contribution is conceptual rather than evidential. Those strengths are real, but the argument currently rests on an unformalized construct and on evidence that is not actually presented in the manuscript. As a research agenda the paper is promising; as a demonstration of compatibility it is not yet conclusive.

major comments (3)

[Section 3] The load-bearing notion of 'derivational likelihood' is asserted but not formally defined. The text defines string likelihood as a Markov product over next-token distributions and says derivational likelihood is 'the probability that a string be derived given all relevant model weights and states' without the Markov assumption. For a standard autoregressive transformer there is no latent derivation or grammar variable in the inference computation: the sequential next-token distribution fully determines the string's probability, and the paper specifies no additional quantity that would count as 'derivational'. The paper concedes that extraction is not currently possible, but the deeper problem is that the quantity itself is left undefined for the model class that motivates the debate. Without a formal definition, the claim that LMs can distinguish grammaticality from string likelihood is unsupported.
[Section 3] Even for grammar induction LMs, where a derivational likelihood is well-defined relative to an induced grammar, the paper does not validate that this quantity tracks grammaticality. A PCFG can assign low probability to rare but grammatical sentences and high probability to ungrammatical ones; the paper proposes no threshold, normalization, or calibration that would identify grammaticality. The sentence 'making a clear distinction between string likelihood and derivational likelihood, or grammaticality' conflates a model-internal score with a human linguistic judgment. The authors should either provide empirical evidence (for example, correlation with human acceptability judgments on minimal pairs) or explicitly mark the identification of derivational likelihood with grammaticality as a hypothesis to be tested.
[Section 3] The empirical support for the claim that some LMs 'decidedly do' learn grammars is not presented in the manuscript. The only concrete demonstration cited is Portelance et al. (2024), a preprint by the first author; the paper provides no experimental details, no summary of results, and no independent confirmation. Because this claim is needed for the argument that grammar induction LMs already constitute successful discovery procedures, the manuscript should at minimum summarize the supporting evidence and its quality, or weaken the claim to a statement about what these models 'may' achieve.

minor comments (5)

[Abstract] The abstract contains a typo: 'wonder about the the compatibility' should read 'wonder about the compatibility'.
[Section 2] The phrase 'rules that transformed them into the output s(Chomsky, 1957, 1965)' appears to have a missing word or formatting error; it should likely be 'the output sentences (Chomsky, 1957, 1965)'.
[Section 3] The sentence 'making derivational likelihood easily accessible, see Figure 4' likely refers to the wrong figure; Figure 4 depicts discovery/decision/evaluation procedures, while the comparison of transformer-based and grammar induction LMs is in Figure 6.
[References] Several references contain formatting errors, notably 'The range of aadequacy' (Chomsky 1956b) and the author fields 'Feiman, R., Mody, S., Sanborn, S., & and, S. C.' and 'Liu, Z., & and, M. J.' in which author names appear to be missing.
[Section 2] The 'cognitive duck test' is introduced as a framing device, but its role could be clarified: it is an analogy for levels of adequacy, not itself an argument or a methodological criterion.

Circularity Check

0 steps flagged · score 2.0 of 10

No definitional circularity in the three compatibility theses; one minor self-citation supports an empirical subclaim about acquisition modeling but is not load-bearing for the central argument.

full rationale

The paper's central claims are conceptual: (1) LMs are formal generative models under Chomsky's original formalization, (2) grammar induction LMs can serve as discovery procedures, and (3) LMs can aid the Minimalist Program. None of these claims is derived from a fitted parameter or an equation whose output is its input. The central bridge in Section 3 is the asserted existence of 'derivational likelihood' distinct from string likelihood; the paper defines this as the probability that a string is derived given all relevant model weights and states, but it concedes that for transformer LMs there is no clear way to extract it. This is an unvalidated premise rather than a circular step: the paper does not define grammaticality as model-internal derivational likelihood, and the claim that this quantity tracks grammaticality is an empirical or interpretive assertion, not a tautology. The only notable self-citation is Portelance, Reddy, and O'Donnell (2024), used to support the claim that grammar induction LMs 'have already been used' to model the acquisition process. That citation supports an empirical subclaim and is not the sole basis for the grammar-induction argument, which also cites independent work (Kim, Dyer, and Rush 2019; Drozdov et al. 2019; Zhu, Bisk, and Neubig 2020). The 'cognitive duck test' is introduced as the authors' own framing device, but it is not used as a premise that presupposes the conclusion; it merely restates the levels of adequacy. Overall, no prediction reduces to an input by construction, and the self-citation is minor rather than load-bearing.

Assumptions & free parameters 0 free parameters · 4 assumptions · 2 invented entities

No free parameters are present. The argument rests on four background assumptions: an interpretive reading of Chomsky's grammar definition, the existence and extractability of derivational likelihood, the representativeness of grammar induction LMs, and the applicability of adequacy levels to LMs. Two invented concepts are introduced: 'derivational likelihood' and the 'cognitive duck test'.

assumptions (4)

domain assumption LMs are formal generative models in Chomsky's original sense.
The paper interprets Chomsky's definition of a grammar as any device that generates a language, and applies it to LMs. This is an interpretive premise about how to read the classical definitions, Section 2.
ad hoc to paper Derivational likelihood exists and is in-principle extractable from LMs.
The paper defines derivational likelihood but admits there is no clear way to extract it from current transformer LMs, Section 3. This is a postulate specific to this paper's argument.
domain assumption Grammar induction LMs are representative of LMs and serve as discovery procedures.
The paper presents grammar induction LMs as 'the best of both worlds' and cites Portelance et al. (2024) as evidence, Section 3. This assumes such models are a reliable class for the claim.
domain assumption Chomsky's three levels of adequacy apply to LMs as scientific models.
The paper extends observational, descriptive, and explanatory adequacy to LMs, Section 2. This is a background assumption about how to evaluate computational models of language.

invented entities (2)

Derivational likelihood
purpose: To distinguish grammaticality from string probability in LMs.
Introduced in Section 3 as the probability of a string given all relevant model weights and states, but with no current method of extraction and no falsifiable prediction.
Cognitive duck test
purpose: To illustrate the three levels of adequacy using the metaphor of a mechanical duck.
Defined in Section 2 by the authors as a heuristic; it has no empirical content.

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Cite this review

Pith. "Pith review of On the Compatibility of Generative AI and Generative Linguistics." pith.science (2026). https://pith.science/paper/WBFVYOOO

@misc{pith2026241110533,
  author       = {Pith},
  title        = {Pith review of: On the Compatibility of Generative AI and Generative Linguistics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WBFVYOOO}},
  note         = {Machine review of arXiv:2411.10533}
}

read the original abstract

In mid-20th century, the linguist Noam Chomsky established generative linguistics, and made significant contributions to linguistics, computer science, and cognitive science by developing the computational and philosophical foundations for a theory that defined language as a formal system, instantiated in human minds or artificial machines. These developments in turn ushered a wave of research on symbolic Artificial Intelligence (AI). More recently, a new wave of non-symbolic AI has emerged with neural Language Models (LMs) that exhibit impressive linguistic performance, leading many to question the older approach and wonder about the the compatibility of generative AI and generative linguistics. In this paper, we argue that generative AI is compatible with generative linguistics and reinforces its basic tenets in at least three ways. First, we argue that LMs are formal generative models as intended originally in Chomsky's work on formal language theory. Second, LMs can help develop a program for discovery procedures as defined by Chomsky's "Syntactic Structures". Third, LMs can be a major asset for Chomsky's minimalist approach to Universal Grammar and language acquisition. In turn, generative linguistics can provide the foundation for evaluating and improving LMs as well as other generative computational models of language.

Figures

Figures reproduced from arXiv: 2411.10533 by the authors.

**Figure 2.** Chomsky-Schützenberger hierarchy Networks (RNNs), Transformer-based LMs, and versions augmented with classical stack or tape memory such as stack-RNN and tape-RNN. They grouped their language recognition tasks based on the ChomskySchützenberger hierarchy to test whether different neural architectures are able to generalize to out-of distribution inputs. They showed that RNNs and Transformers models fail to general… view at source ↗

**Figure 3.** The structural ambiguity in the phrase “pancakes or bacon and eggs" captured by a context free [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Procedures for linguistic theory development. Inspired by the original figure from [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: The relative positions of both linguistic and neural network based generative theories of language [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: The differences between transformer-based LMs and grammar induction LMs in terms of their [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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