1 citations · 1 across the 4 of their papers we have counts for
4 papers
What You Prompt is What You Get: Increasing Transparency of Prompting Using Prompt Cards
Amandine M. Caut, Beimnet Zenebe, Amy Rouillard +1
The rapid advancement and impressive capabilities of large language models (LLMs) have given rise to the field of prompt engineering, the practice of crafting inputs to guide LLMs…
Representing data in words: A context engineering approach
Amandine M. Caut, Amy Rouillard, Beimnet Zenebe +3
Large language models (LLMs) have demonstrated remarkable potential across a broad range of applications. However, producing reliable text that faithfully represents data remains a…
Properties of the `friend of a friend' model for network generation
Tiffany Y. Y. Lo, Watson Levens, David J. T. Sumpter
The way in which a social network is generated, in terms of how individuals attach to each other, determines the properties of the resulting network. Here we study an intuitively a…
A minimal model of cognition based on oscillatory and current-based reinforcement processes
Linnéa Gyllingberg, Yu Tian, David J. T. Sumpter
Building mathematical models of brains is difficult because of the sheer complexity of the problem. One potential starting point is through basal cognition, which gives abstract re…