Conditional graphic layout generation, which automatically maps user constraints to high-quality layouts, has attracted widespread attention today. Although recent works have achieved promising performance, the lack of versatility and data efficiency hinders their practical applications. In this work, we propose LayoutPrompter, which leverages large language models (LLMs) to address the above problems through in-context learning. LayoutPrompter is made up of three key components, namely input-output serialization, dynamic exemplar selection and layout ranking. Specifically, the input-output serialization component meticulously designs the input and output formats for each layout generation task.

Conditional graphic layout generation, which automatically maps user constraints to high-quality layouts, has attracted widespread attention today. Although recent works have achieved promising performance, the lack of versatility and data efficiency hinders their practical applications. In this work, we propose LayoutPrompter, which leverages large language models (LLMs) to address the above problems through in-context learning. LayoutPrompter is made up of three key components, namely input-output serialization, dynamic exemplar selection and layout ranking. Specifically, the input-output serialization component meticulously designs the input and output formats for each layout generation task.

This means that 2020 will be an important year for the next decade of innovations in the AI space to set the tone and continue the current momentum. But what does this mean for organizations selling and buying AI solutions? In which areas should they invest? IDC and Forrester issued lately their forecasts for artificial intelligence (AI) in 2020 and beyond. While outside "market events" can make firms cautious about AI, says Forrester, "brave ones" will continue to invest and expand the first "timid" measures they took in 2019.

April 12, 2017 was the first time I was accused of machine learning. It was mid-morning, mid-class at Stanford University's d.school. Nine graduate students were taking shifts in front of a white board, moving and clustering sticky notes, scanning for connections amongst lessons scribbled upon each. Zoom in, circle a group of like ideas, and write a headline about how they're related. Zoom out, read the headlines, zoom in, erase and explode a grouping that isn't working, make a new one. We had a nice flow going. And then, one of my students said, "This is just like machine learning."