In 2023, Noam Chomsky, considered the founder of modern linguistics, wrote that LLMs "learn humanly possible and humanly impossible languages with equal facility." However, in the Mission: Impossible Language Models paper that received a Best Paper award at the 2024 Association of Computational Linguistics (ACL) conference, researchers shared the results of their testing of Chomsky's theory, having discovered that language models actually struggle with learning languages with non-standard characters. Rogers Jeffrey Leo John, CTO of DataChat Inc., a company that he cofounded while working at the University of Wisconsin as a data science researcher, said the Mission: Impossible paper challenged the idea that LLMs can learn impossible languages as effectively as natural ones. "The models [studied for the paper] exhibited clear difficulties in acquiring and processing languages that deviate significantly from natural linguistic structures," said John. "Further, the researchers' findings support the idea that certain linguistic structures are universally preferred or more learnable both by humans and machines, highlighting the importance of natural language patterns in model training. This finding could also explain why LLMs, and even humans, can grasp certain languages easily and not others."

Studies with impossible languages falsify LMs as models of human language Jeffrey S. Bowers, School of Psychology and Neuroscience, University of Bristol Jeff Mitchell, School of Engineering and Informatics, University of Sussex Commentary on Futrell, R., & Mahowald, K. (in press). How linguistics learned to stop worrying and love the language models. Abstract According to Futrell and Mahowald (F&M), both infants and language models (LMs) find attested languages easier to learn than "impossible languages" that have unnatural structures. We review the literature and show that LMs often learn attested and many impossible languages equally well. Difficult to learn impossible languages are simply more complex (or random).

Linguistic commentary on LLMs, heavily influenced by the theoretical frameworks of de Saussure and Chomsky, is often speculative and unproductive. Critics challenge whether LLMs can legitimately model language, citing the need for "deep structure" or "grounding" to achieve an idealized linguistic "competence." We argue for a radical shift in perspective towards the empiricist principles of Witold Mańczak, a prominent general and historical linguist. He defines language not as a "system of signs" or a "computational system of the brain" but as the totality of all that is said and written. Above all, he identifies frequency of use of particular language elements as language's primary governing principle. Using his framework, we challenge prior critiques of LLMs and provide a constructive guide for designing, evaluating, and interpreting language models.

We present a preview of the Syntactic Acceptability Dataset, a resource being designed for both syntax and computational linguistics research. In its current form, the dataset comprises 1,000 English sequences from the syntactic discourse: Half from textbooks and half from the journal Linguistic Inquiry, the latter to ensure a representation of the contemporary discourse. Each entry is labeled with its grammatical status ("well-formedness" according to syntactic formalisms) extracted from the literature, as well as its acceptability status ("intuitive goodness" as determined by native speakers) obtained through crowdsourcing, with highest experimental standards. Even in its preliminary form, this dataset stands as the largest of its kind that is publicly accessible. We also offer preliminary analyses addressing three debates in linguistics and computational linguistics: We observe that grammaticality and acceptability judgments converge in about 83% of the cases and that "in-betweenness" occurs frequently. This corroborates existing research. We also find that while machine learning models struggle with predicting grammaticality, they perform considerably better in predicting acceptability. This is a novel finding. Future work will focus on expanding the dataset.

Considering the proliferation of responses to Piantadosi's original paper and the ongoing debate sparked by this special issue of the Italian Journal of Linguistics, it is clear that the discussion has touched a raw nerve in linguistic theorizing . In the original target paper (Chesi, this issue), I illustrated three prototypical (and in many respects, extreme) positions -- the computational, theoretical, and experimental perspectives -- without explicitly endorsing any of them. Instead, I attempted to highlight what I believe are the key weaknesses o f each of these prototypical stances, ultimately concluding that formal (i.e., ' generative ') linguistics -- more specifically, Minimalis m, my theoretical comfort zone -- must adopt practices and tools that are common in both computational and experimental fields . As noted by most respondents, the title and some of the more extreme statements were intended as mild provocations to draw attention to core issues affecting linguistic theorizing . M y position -- somehow obscured behind the ' three - body problem ' -- is that any relevant scientific progress is driven by theoretical insight, not by trawling using experimental or computational methods that are cost - inefficient, energy - intensive, and ultimately unsustainable . Moreover, in full agreement with most of the replies, I believe that the success of certain large language models (L L Ms), which are based on specific architectural assumptions, do es not constitute a refutation of the generative paradigm. On the contrary, it strongly supports several key intuitions that have emerged within the generative linguistic tradition (Rizzi this issue) . H owever, a concrete problem of ' incommensurability ' arises (Hao this issue), as differing methodologies and specialized jargon (Butt this issue) often result in circular, unresolved discussions .

Chomsky (1968: 3) greeted the rise of computing technology with skepticism, arguing that "the kinds of structures that are realizable in terms of [computational methods ] are simply not those that must be postulated to underlie the use of language . " 55 years later, Piantadosi (2023: 15) celebrated the release of ChatGPT by directing that same criticism toward generative linguistic s: "the success of large language models is a failure for generative theories because it goes against virtually all of the principles these theories have espoused . " Chesi ( forthcoming) may not agree with Piantadosi's criticisms, but he does take them as a harbinger of scientific crisis. The minimalist program, hampered by a lack of formal and empirical rigor, has failed to produce a comprehensive, self - consistent theory of syntax. ChatG PT's apparent linguistic competence, in tandem with the success of computational accounts of gradient acceptability and online phenomena, seem to suggest that "generative linguistics no longer dictates the agenda for future linguistic challenges" ( Chesi forthcoming: 2). In order to survive, Chesi warns, generativists need to make progress towards a theory that is based on precisely stated principles and evaluated on a common set of explananda . Chesi's target paper presents the current collision of the worlds as a debate about the intellectual merits of generativist theories. According to Chesi, the success of generativism depends on generativists' ability to resolve their deficits of rigor, so that they can parry the theoretical attacks that language model s have levied against core principles of minimalism. This response argues, contrary to Chesi's framing but consistent with current consensus in the history and sociology of science (Fleck 1935; Kuhn 1962; Mullin s 1975; Latour 1984; Law & Lodge 1984), that the generativist crisis described by Piantadosi and Chesi is social in nature, and cannot be averted by intellectual means.

It's 1968, and Norm and Claudette are having lunch. Norm is explaining his position that all human languages share deep underlying structure and has worked out careful theories showing how the surface forms of language can be derived from these underlying principles. Claudette, whose favorite movie is the recently released 2001: A Space Odyssey and who particularly loves the HAL character, wants to make machines that could talk with us in any human language. Claudette asks Norm whether Norm thinks his theories could be useful for building such a system. Norm says he is interested in human language and the human mind, found HAL creepy, and isn't sure why Claudette is so interested in building chatbots or what good would come of that. Nonetheless, they both agree that it seems likely that, if Norm's theories are right (and he sure thinks they are!), they could be used to work out the fundamental rules and operations underlying human language in general--and that should, in principle, prove useful for building Claudette's linguistic machines. Claudette is very open to this possibility: all she wants is a machine that talks and understands. She doesn't really care how it happens. Norm and Claudette have very different goals, but they enjoy their conversations and are optimistic that they can both help each other.

Language is the cornerstone of human communication, and the complexity of language lies in the diversity and recursion of its structure. Chomsky (1957) introduced the concept of recursion into natural language, arguing that the grammar in human natural language was a finite set of recursive rules by which an infinite number of linguistic expressions could be generated. In Corballis' (2014) words, the claim that recursion is the essence of natural language has been a continuing theme of Chomsky's work since his 1957 book Syntactic Structures. This theme is reiterated in Hauser et al. (2002), proposing that the faculty of language in the narrow sense only includes recursion, the only uniquely human component of the faculty of language. This proposal is summarized as the "recursion-only hypothesis" in Jackendoff and Pinker (2005: 212), which highlights the importance of recursion in linguistics. In spited of the lack of a consistent definition of (linguistic) recursion in the literature, most literature involves category recursion, which is defined as the "embedding of a category inside another of the same category". For instance, Martins and Fitch (2014) claim that recursion has been used to characterize the process of embedding a constituent of a certain kind of category inside another constituent of the same kind. This "embedding" process naturally generates hierarchical structures that display similar properties across different levels of embedding, and, thus, the feature of "self-similarity" is a signature of recursive structures. To illustrate that, they hold that the compound noun [[student] committee] (which has the structure [[[A]A] ]) is recursive since a noun phrase (NP) is embedded inside another NP, while a sentence with a noun plus a verb such as [[trees] grow] (which has the structure [[[A]B] ]) is non-recursive since a constituent of a given type of category is not embedded within a constituent of that same type.

A significant debate has emerged in response to a paper written by Steven Piantadosi (Piantadosi, 2023) and uploaded to the LingBuzz platform, the open archive for generative linguistics. Piantadosi's dismissal of Chomsky's approach is ruthless, but generative linguists deserve it. In this paper, I will adopt three idealized perspectives -- computational, theoretical, and experimental -- to focus on two fundamental issues that lend partial support to Piantadosi's critique: (a) the evidence challenging the Poverty of Stimulus (PoS) hypothesis and (b) the notion of simplicity as conceived within mainstream Minimalism. In conclusion, I argue that, to reclaim a central role in language studies, generative linguistics -- representing a prototypical theoretical perspective on language -- needs a serious update leading to (i) more precise, consistent, and complete formalizations of foundational intuitions and (ii) the establishment and utilization of a standardized dataset of crucial empirical evidence to evaluate the theory's adequacy. On the other hand, ignoring the formal perspective leads to major drawbacks in both computational and experimental approaches. Neither descriptive nor explanatory adequacy can be easily achieved without the precise formulation of general principles that can be challenged empirically.

This study sets out to answer one major question: Who thinks better, non-native speakers of English or ChatGPT?, providing evidence from processing and interpreting center-embedding English constructions that human brain surpasses ChatGPT, and that ChatGPT cannot be regarded as a theory of language. Fifteen non-native speakers of English were recruited as participants of the study. A center-embedding English sentence was presented to both the study participants and ChatGPT. The study findings unveil that human brain is still far ahead of Large Language Models, specifically ChatGPT, even in the case of non-native speakers of an L2, here English. The study concludes that human brain's ability to process and interpret natural language data is unique and that ChatGPT still lags behind this human unique ability.