| > it doesn't know how to look at what it's written and decide if it matches its intent, or whether it'll break consistency or get in the way later. And we can build other models specifically for this. We don't need to add this stuff to GPT-3; GPT-3 can literally act as a part, a component. GPT-3 can serve the role in a larger model that "imagination" does in a human brain—being fed inputs; having corresponding outputs scavenged through by the rest of the model; and then being "fed back" with input that relates to the scavenged outputs. One thing I'd be very curious to see tried, is to get a system consisting of GPT-3 as "writer", and some other (summarization?) model as "editor", to attempt to dramatize or adapt into prose fiction, a machine-readable sequence of events (e.g. a machinima recording of a stage-play enacted within an MMO game.) We already have models that turn machine-readable sequences of events directly into prose; see e.g. baseball news reporting. Such models can work just as well in reverse, summarizing in-domain prose back into machine-readable facts. So if you take such a prose-to-factual-assertions "reading comprehension" model, and feed it GPT-3's output; and then measure the distance between the set of events comprehended by the "reading comprehension" model from GPT-3's output, and the source data (which is also in the form of a set of factual assertions), then you can iterate GPT-3 — maybe even one additional line of prose at a time — to find a story that is a consistent adaptation of the source. In this sense, GPT-3 is acting as a programmer, and the "reading comprehension" model as a compiler — with the compiler reaching out and erasing any line that doesn't compile. Of course, you're limited in this by the "reading level" of the reading-comprehension model. But this is also true of humans; you can't get out a literary classic if the writer's editor and alpha-readers were five-year-olds. |