LLMs' flexibility is largely due to an "in-context learning" effect that emerges when

We also demonstrate its effectiveness in generating robotic tasks.

Unlike existing work focusing on the influence of scale on base capabilities, our work examines the influence of architecture on those. Specifically, our concern is: How does architecture influence the base capabilities of pre-trained language models?

Evaluation benchmarks are at the core role for AI development.

Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format.