arXiv:2605.23039v1 Announce Type: cross Abstract: How do learners acquire knowledge of what is unacceptable without negative evidence? Construction Grammar proposes statistical preemption: exposure to a conventional form (e.g., "donated the books to the library") preempts structurally possible but unattested alternatives ("*donated the library the books"). We present a computational study that, for the first time, directly dissociates statistical preemption from the competing entrenchment hypothesis in large language models within a single converging design. Across four experiments spanning 12
The proliferation of advanced LLMs necessitates deeper understanding of their learning mechanisms, especially concerning areas like 'unacceptable' outputs, which is becoming critical for deployment and safety.
Understanding how LLMs acquire or circumvent negative evidence, specifically through statistical preemption, is fundamental to developing more robust, safe, and explainable AI systems.
This research provides a refined understanding of LLM learning, moving beyond superficial observations to identify underlying causal mechanisms that dictate their linguistic and potentially ethical boundaries.
- · AI Safety Researchers
- · NLP Researchers
- · Companies developing LLMs
- · Researchers relying on purely correlational LLM analysis
Improved models for predicting and preventing LLM generation of harmful or 'unacceptable' content will emerge.
New architectural designs or training methodologies might be developed to more effectively integrate or simulate statistical preemption.
A more profound understanding of artificial linguistic acquisition could inform theories of human language development, bridging cognitive science and AI.
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Read at arXiv cs.LG