arXiv:2502.18795v4 Announce Type: replace Abstract: Do language models (LMs) offer insights into human language learning? A common argument against this idea is that because their architecture and training paradigm are so vastly different from humans, LMs can learn arbitrary inputs as easily as natural languages. We test this claim by training LMs to model impossible and typologically unattested languages. Unlike previous work, which has focused exclusively on English, we conduct experiments on 12 languages from 4 language families with two newly constructed parallel corpora. Our results show
This research is emerging as the field grapples with the fundamental capabilities and limitations of large language models, particularly their relationship to human cognition and universal grammar, as AI development accelerates, raising questions about explainability and alignment.
Understanding whether LLMs learn language like humans or through fundamentally different mechanisms is crucial for developing more robust, generalizable, and potentially safer AI, influencing investment in AI research directions and the conceptualization of AI intelligence.
This research challenges the assumption that LMs can learn arbitrary inputs with equal ease, suggesting there might be inherent biases or structures that make some 'languages' easier or harder to model, aligning their learning more closely with human language acquisition constraints.
- · AI ethicists and philosophers
- · Linguistics researchers
- · AI safety and alignment research labs
- · Multilingual AI developers
- · Developers relying on 'anything goes' assumptions for AI capabilities
- · Models designed without linguistic constraints
- · Simplified views of AI learning
This study suggests that large language models might inherently struggle with impossible or typologically unattested language features, even if the architecture could theoretically support them.
This insight could lead to the development of new AI architectures or training methodologies that either leverage or mitigate these linguistic 'constraints' for more efficient and human-like language processing.
It might influence the debate on whether AI can truly achieve human-level intelligence or consciousness by highlighting a potential cognitive commonality with human language learners, guiding AI regulatory frameworks based on these findings.
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Read at arXiv cs.CL