Isabelle Levent

The fact that adding keywords like  Let’s Think Step By Step , adding “Greg Rutkowski”, prompt weights, and even negative prompting are still so enormously effective, is a sign that we are nowhere close to perfecting the “language” part of “large language models”.

For a computer to make a subtle combinational joke, never mind to assess its tastefulness, would require, first, a data-base with a richness comparable to ours, and, second, methods of link-making (and link-evaluating) comparable in subtlety with ours. 

We find that models learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively “good” prompts. Further, such patterns hold even for models as large as 175 billion parameters (Brown et al., 2020) as well as the recently proposed instruction-tuned models which are trained on

“There are real concerns with respect to the copyright of outputs from these models and unaddressed rights issues with respect to the imagery, the image metadata and those individuals contained within the imagery,” said Peters.

Many methods for creating these models don't (and to be honest can't) attach the name, website and other details of every image and piece of text used to a create a new image in the metadata to every step of the process.

Strict synonymy, or even a drop-in replacement word, is not essential for most writers, who instead may use a thesaurus to expand an idea or discover a new one. 

Imagine an art-lover at an exhibition entitled ‘Dots 2008’. He speaks to two artists, each displaying a painting. In both cases, the art-lover cannot see past the seemingly random arrangement of dots of paint. He mentions this to the first artist, who says: “Oh, no, they’re not randomly placed. Each dot represents a friend of mine. The colour of

If it was possible to deduce how much of an influence each individual image has on the final outcome (and the owner of each image was known and labelled, which I currently doubt happens), would it be simple to compensate people then?