arXiv:2606.02953v1 Announce Type: new Abstract: Usage-based theories of grammars posit that creative productivity of the structures of language is both bolstered and constrained by two distinct frequency signals: entrenchment, stemming from high frequency usage, and preemption, stemming from having never observed a particular linguistic structure in a context where one might expect that structure to appear. Large Language Models are also usage-based, in the sense that the structures of language are learned through exposure to vast amounts of text. Here, we test whether or not the opposing stat
The proliferation of Large Language Models (LLMs) and their increasing integration into various applications necessitates a deeper understanding of their linguistic capabilities and limitations.
Understanding how LLMs generate and interpret language affects their design, reliability, and the potential for their autonomous deployment in critical contexts.
This research refines our understanding of LLM linguistic productivity, highlighting fundamental differences from human language acquisition and generation, particularly regarding 'preemption'.
- · AI researchers
- · NLP developers
- · Linguistics academia
- · Developers relying on naive human linguistic models for LLMs
Further research will likely explore methods to instill preemptive linguistic capabilities into LLMs.
This could lead to more robust and less error-prone LLMs, particularly in fields requiring precise and contextually aware language generation.
Improved linguistic understanding in LLMs might accelerate the development of truly autonomous AI agents capable of complex communication.
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Read at arXiv cs.CL