A controversial test for Large Language Models concerns the ability to discern possible from impossible language. While some evidence attests to the models' sensitivity to what crosses the limits of grammatically impossible language, this evidence has been contested on the grounds of the soundness of the testing material. We use model-internal representations to tap directly into the way Large Language Models represent the 'grammatical-ungrammatical' distinction. In a novel benchmark, we elicit probabilities from 4 models and compute minimal-pair surprisal differences, juxtaposing probabilities assigned to grammatical sentences to probabilities assigned to (i) lower frequency grammatical sentences, (ii) ungrammatical sentences, (iii) semantically odd sentences, and (iv) pragmatically odd sentences. The prediction is that if string-probabilities can function as proxies for the limits of grammar, the ungrammatical condition will stand out among the conditions that involve linguistic violations, showing a spike in the surprisal rates. Our results do not reveal a unique surprisal signature for ungrammatical prompts, as the semantically and pragmatically odd conditions consistently show higher surprisal. We thus demonstrate that probabilities do not constitute reliable proxies for model-internal representations of syntactic knowledge. Consequently, claims about models being able to distinguish possible from impossible language need verification through a different methodology.

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.

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.

Typical models of learning assume incremental estimation of continuously-varying decision variables like expected rewards. However, this class of models fails to capture more idiosyncratic, discrete heuristics and strategies that people and animals appear to exhibit. Despite recent advances in strategy discovery using tools like recurrent networks that generalize the classic models, the resulting strategies are often onerous to interpret, making connections to cognition difficult to establish. We use Bayesian program induction to discover strategies implemented by programs, letting the simplicity of strategies trade off against their effectiveness. Focusing on bandit tasks, we find strategies that are difficult or unexpected with classical incremental learning, like asymmetric learning from rewarded and unrewarded trials, adaptive horizon-dependent random exploration, and discrete state switching.

Humans can learn new concepts from a small number of examples by drawing on their inductive biases. These inductive biases have previously been captured by using Bayesian models defined over symbolic hypothesis spaces. Is it possible to create a neural network that displays the same inductive biases? We show that inductive biases that enable rapid concept learning can be instantiated in artificial neural networks by distilling a prior distribution from a symbolic Bayesian model via meta-learning, an approach for extracting the common structure from a set of tasks. By generating the set of tasks used in meta-learning from the prior distribution of a Bayesian model, we are able to transfer that prior into a neural network. We use this approach to create a neural network with an inductive bias towards concepts expressed as short logical formulas. Analyzing results from previous behavioral experiments in which people learned logical concepts from a few examples, we find that our meta-trained models are highly aligned with human performance.

We present a critical assessment of Piantadosi's (2023) claim that "Modern language models refute Chomsky's approach to language," focusing on four main points. First, despite the impressive performance and utility of large language models (LLMs), humans achieve their capacity for language after exposure to several orders of magnitude less data. The fact that young children become competent, fluent speakers of their native languages with relatively little exposure to them is the central mystery of language learning to which Chomsky initially drew attention, and LLMs currently show little promise of solving this mystery. Second, what can the artificial reveal about the natural? Put simply, the implications of LLMs for our understanding of the cognitive structures and mechanisms underlying language and its acquisition are like the implications of airplanes for understanding how birds fly. Third, LLMs cannot constitute scientific theories of language for several reasons, not least of which is that scientific theories must provide interpretable explanations, not just predictions. This leads to our final point: to even determine whether the linguistic and cognitive capabilities of LLMs rival those of humans requires explicating what humans' capacities actually are. In other words, it requires a separate theory of language and cognition; generative linguistics provides precisely such a theory. As such, we conclude that generative linguistics as a scientific discipline will remain indispensable throughout the 21st century and beyond.

The Abstraction and Reasoning Corpus (ARC) is a challenging program induction dataset that was recently proposed by Chollet (2019). Here, we report the first set of results collected from a behavioral study of humans solving a subset of tasks from ARC (40 out of 1000). Although this subset of tasks contains considerable variation, our results showed that humans were able to infer the underlying program and generate the correct test output for a novel test input example, with an average of 80% of tasks solved per participant, and with 65% of tasks being solved by more than 80% of participants. Additionally, we find interesting patterns of behavioral consistency and variability within the action sequences during the generation process, the natural language descriptions to describe the transformations for each task, and the errors people made. Our findings suggest that people can quickly and reliably determine the relevant features and properties of a task to compose a correct solution. Future modeling work could incorporate these findings, potentially by connecting the natural language descriptions we collected here to the underlying semantics of ARC.

Humans surpass the cognitive abilities of most other animals in our ability to "chunk" concepts into words, and then combine the words to combine the concepts. In this process, we make "infinite use of finite means", enabling us to learn new concepts quickly and nest concepts within each-other. While program induction and synthesis remain at the heart of foundational theories of artificial intelligence, only recently has the community moved forward in attempting to use program learning as a benchmark task itself. The cognitive science community has thus often assumed that if the brain has simulation and reasoning capabilities equivalent to a universal computer, then it must employ a serialized, symbolic representation. Here we confront that assumption, and provide a counterexample in which compositionality is expressed via network structure: the free category prior over programs. We show how our formalism allows neural networks to serve as primitives in probabilistic programs. We learn both program structure and model parameters end-to-end.

Every year, more than a billion people around the world celebrate Chinese New Year and engage in a subtle linguistic dance with luck. You can think of it as a set of holiday rituals that resemble a courtship. To lure good fortune into their lives, they may decorate their homes and doors with paper cutouts of lucky words or phrases. Those who need a haircut make sure to get one before the New Year, as the word for "hair" (fa) sounds like the word for "prosperity"--and who wants to snip away prosperity, even if it's just a trim? The menu of food served at festive meals often includes fish, because its name (yu) sounds the same as the word for "surplus"; a type of algae known as fat choy because in Cantonese it sounds like "get rich"; and oranges, because in certain regions their name sounds like the word for "luck."