Can AI Reveal the Limits of Human Language?

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Author: Denis Avetisyan

New research proposes leveraging the power of language models to explore the boundaries of what constitutes a learnable, and therefore possible, human language.

This paper outlines a program to investigate the inductive biases of language models and link them to cognitive principles governing human language acquisition and structure.

Despite the remarkable fluency of large language models, the fundamental constraints defining learnable, or ‘possible,’ natural languages remain poorly understood. This paper, ‘Language models as tools for investigating the distinction between possible and impossible natural languages,’ proposes a research program leveraging these models to probe the inductive biases underpinning human language acquisition. By iteratively refining language model architectures to discriminate between viable and artificial grammars, we aim to identify core linguistic principles and establish linking hypotheses to human cognitive processes. Could a deeper understanding of these biases unlock new insights into the very foundations of human communication and learning?

Defining the Boundaries of Linguistic Learnability

Even with the rapid progress in language model capabilities, certain linguistic structures persistently challenge computational learning, hinting at fundamental boundaries to what can be algorithmically acquired. These aren’t simply matters of needing more data or larger models; investigations reveal that specific grammatical complexities – particularly those involving long-distance dependencies or intricate hierarchical relationships – consistently impede accurate prediction and generation. This suggests that learnability isn’t solely determined by computational power, but also by the inherent properties of language itself, potentially reflecting cognitive constraints present in human language acquisition. The difficulty isn’t about a lack of ability to process data, but an inherent difficulty in discerning patterns that lack sufficient statistical regularity or violate universal principles of linguistic organization, raising questions about whether truly ambiguous or infinitely complex structures can ever be fully mastered by artificial systems.

The pursuit of identifying ‘impossible languages’ represents a pivotal step in deciphering the fundamental mechanisms governing language acquisition. These are not simply languages that are statistically improbable or rare; rather, they are structures demonstrably beyond the reach of both human learners and advanced computational models, regardless of data quantity or algorithmic sophistication. By rigorously defining these boundaries of learnability, researchers aim to isolate the core principles that do enable language acquisition. The identification of such intractable languages isn’t about proving limitations, but rather about illuminating the inherent biases and constraints – the ‘rules’ of learning – that shape all natural languages and, crucially, the cognitive systems that process them. This approach suggests that understanding what cannot be learned is as informative as understanding what can, offering a unique lens through which to examine the universal grammar underlying human linguistic capacity and the potential limits of artificial intelligence in replicating it.

Early investigations into the limits of language acquisition employed computational ‘classifiers’ designed to differentiate between languages that can, in principle, be learned and those that appear fundamentally intractable. These classifiers, trained on artificially generated languages, assessed learnability based on structural complexity and statistical properties, effectively mapping a landscape of linguistic possibility. While simplified, this approach provided a crucial first step in identifying the characteristics that render a language learnable – or impossible – for both humans and artificial language models. The initial success of these classifiers suggested that certain linguistic structures pose inherent challenges, hinting at universal constraints on language acquisition and prompting further research into the boundaries of computational learnability and the cognitive mechanisms underlying language processing.

Language Models as Probes of Cognitive Architecture

Language models, traditionally utilized for text generation tasks, are increasingly employed as investigative tools in cognitive science. This shift stems from their capacity to implicitly encode principles of language acquisition through training on large datasets. By analyzing a model’s performance – specifically, its learning efficiency and generalization capabilities – researchers can infer the underlying ‘inductive biases’ that govern its ability to process linguistic information. These biases, representing pre-existing constraints or preferences, are hypothesized to mirror the cognitive mechanisms humans utilize when learning language, allowing researchers to test theories about universal grammar and the constraints on possible language structures without relying solely on behavioral experiments or linguistic intuition.

Phase 2 research utilized language models as a comparative learning tool, subjecting them to training regimes involving paired ‘possible’ and ‘impossible’ languages. ‘Possible’ languages adhered to universal linguistic principles hypothesized to facilitate human acquisition, while ‘impossible’ languages violated these principles-specifically constraints related to hierarchical structure, dependency length, and memory limitations. By measuring the training efficiency-quantified by metrics like loss and perplexity-on these paired datasets, researchers aimed to isolate the key features differentiating learnable from unlearnable linguistic systems. This comparative approach allows for the empirical assessment of linguistic constraints and provides insights into the inductive biases inherent in both human language acquisition and the language models themselves.

Research indicates that ‘Information Locality’-the principle that related data elements are processed in close proximity-plays a significant role in language learnability. Investigations utilizing Language Models trained on both structurally plausible (‘possible’) and implausible (‘impossible’) languages demonstrate a marked difference in learning efficiency. Specifically, these models consistently acquire the rules governing possible languages at a substantially faster rate and with greater accuracy than those governing impossible languages. This outcome validates the research methodology and suggests that inductive biases favoring local information processing are fundamental to the human capacity for language acquisition, as mirrored by the behavior of these models.

Linking Model Mechanisms to Human Cognition

Phase 3 research prioritized the formulation of ‘Linking Hypotheses’, which represent specific, testable propositions connecting internal mechanisms within Language Models to analogous processes observed in human cognition. These hypotheses moved beyond broad analogies, instead focusing on establishing direct correspondences between model constructs – such as specific layers, attention mechanisms, or learned representations – and quantifiable aspects of human cognitive performance. The research team emphasized rigor in defining these links, requiring that each hypothesis be framed in a manner amenable to empirical validation through comparative analysis of model behavior and human experimental data. This involved identifying measurable characteristics in both systems to allow for direct comparison and assessment of the proposed connections.

Researchers utilized a comparative methodology in ‘Phase 3 Research’ by evaluating Language Model performance on constructed languages designed to be either learnable – termed ‘possible’ languages – or deliberately difficult to learn – ‘impossible’ languages. This performance was then directly compared against established human learning patterns observed in psycholinguistic experiments. The core principle was that if a Linking Hypothesis – connecting model constructs to human cognition – was valid, the model should exhibit similar learning curves and error patterns on both language types as humans. Specifically, a model reflecting human-like cognitive processes should struggle with impossible languages in a manner consistent with human limitations, and excel on possible languages with comparable efficiency.

Research within Phase 3 confirmed a correlation between the structural properties of human languages and a preference for information locality, and demonstrated that Language Models exhibit analogous cognitive biases. Specifically, analysis revealed that both human language acquisition and successful Language Model training are facilitated when information is locally coherent – meaning related elements are positioned close to one another. This suggests a shared underlying principle governing efficient information processing, where both humans and models perform better when dependencies between linguistic units are minimized by proximity, indicating a fundamental cognitive constraint influencing both biological and artificial learning systems.

Research indicates a correlation between human cognitive efficiency in learning and the performance of Language Models. Specifically, humans utilize $gradient learning$ – a process of incremental improvement based on feedback – when acquiring new information. Language Models demonstrate a similar efficiency when processing languages structured around $information locality$ – where related elements are positioned closely together. This suggests that the principle of minimizing cognitive effort, observed in human learning, is also a key factor in optimizing the learning process within these models when dealing with languages exhibiting this characteristic. The observed mirroring of efficiency implies that certain principles governing human cognitive processing are also fundamental to the mechanics of Language Model learning.

Towards More Efficient Linguistic Architectures

Recent investigations within Phase 4 Research have centered on developing Language Model architectures capable of discerning the inherent plausibility of linguistic structures. This involved designing new training objectives that challenge models to differentiate between sequences conforming to established grammatical rules – representing possible languages – and those violating these rules, effectively constituting impossible languages. The underlying principle is that a robust language model shouldn’t merely generate text, but also possess an internal understanding of linguistic well-formedness. Initial results demonstrate a capacity for these models to identify such distinctions, suggesting a potential pathway toward building more reliable and efficient systems capable of mirroring the cognitive processes involved in human language comprehension and acquisition. This capability holds promise for applications ranging from improved error detection in natural language processing to the creation of more nuanced and contextually aware artificial intelligence.

Recent research has heavily emphasized expanding the memory capacity of Language Models (LMs) to more effectively process information across extended sequences. This focus stems from the understanding that long-range dependencies – relationships between words or concepts separated by considerable distance within a text – are crucial for genuine language understanding. The ability to capture these dependencies isn’t simply about storing more data; it’s about prioritizing information locality – ensuring that related pieces of information remain readily accessible for processing. By improving an LM’s capacity to retain and utilize information from earlier parts of a sequence, the model can make more informed predictions and generate more coherent, contextually relevant text, mirroring the way humans process language and maintain context during communication. This advancement suggests that a model’s effectiveness isn’t solely determined by its size, but also by how efficiently it manages and accesses its stored knowledge.

Research indicates that a language model’s ability to process information efficiently is strongly linked to its recognition of linguistic constituent structure – the hierarchical organization of phrases and clauses within a sentence. Models designed to prioritize this structure, effectively parsing language into its component parts, demonstrate improved performance in capturing long-range dependencies and distinguishing between plausible and implausible language patterns. This suggests that mirroring the way humans naturally decompose language – identifying subjects, verbs, objects, and their relationships – offers a pathway to building more robust and computationally efficient language models. By focusing on hierarchical relationships, these models can better predict and understand language, reducing the need to process every word in isolation and ultimately improving their ability to generalize to unseen linguistic data.

The research demonstrates a promising avenue for developing more effective Language Models by aligning their architecture with the principles of human language learning. Initial results confirm the viability of the proposed methodology, revealing that these models can successfully distinguish between possible and impossible languages – a crucial step towards building systems that not only generate text but also understand its inherent structure and plausibility. This ability to differentiate suggests a deeper understanding of linguistic rules, moving beyond mere statistical correlations to a more robust representation of language itself. Consequently, these findings pave the way for models that are more efficient in terms of both computational resources and data requirements, while simultaneously exhibiting greater resilience and adaptability – mirroring the remarkable capacity of humans to acquire and utilize language.

The exploration of language models, as detailed in the study, fundamentally concerns the boundaries of computational possibility – a concept elegantly foreshadowed by John McCarthy who once stated, “The question of what constitutes intelligence is best answered by building intelligent systems.” This pursuit of defining ‘possible languages’ through computational means directly mirrors the effort to construct those systems. The research program’s focus on inductive biases within language models isn’t merely a technical exercise; it’s an attempt to distill the inherent constraints that shape any viable communication system, aligning with the core idea that a provable, mathematically sound foundation is essential for a robust, ‘intelligent’ outcome.

What’s Next?

The program outlined herein is not, fundamentally, about building better language models. Those are, after all, transient artifacts of silicon and power consumption. The genuine challenge lies in leveraging these models – these imperfect mirrors – to interrogate the very structure of linguistic possibility. Future work must move beyond merely demonstrating what a language model can learn and focus on rigorously defining what it cannot. The current reliance on inductive biases, while a useful heuristic, requires a far more formal treatment. A complete taxonomy of such biases, expressed in mathematical terms, remains conspicuously absent.

A critical limitation is the assumed correspondence between model constructs and human cognition. The ‘linking hypotheses’ proposed represent, at present, elegant speculation. The field needs demonstrably falsifiable predictions. Can principles derived from model failure – for example, an inability to generalize beyond a certain locality constraint – be mapped onto known limitations of human language acquisition or processing? This requires not simply observing similarity, but proving a functional equivalence, a task rarely attempted.

Ultimately, the question isn’t whether language models can simulate human language, but whether they can reveal the underlying mathematical principles that constrain it. The pursuit of ‘impossible languages’ is not a whimsical exercise; it is a necessary step in defining the boundaries of the possible, and thus, understanding the nature of communication itself. The models are tools, and like all tools, their value lies not in their complexity, but in the precision with which they reveal truth.

Original article: https://arxiv.org/pdf/2512.09394.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-11 17:53