I’m interested in automatically generating lengthy, coherent stories of 100,000+ words from a single prompt using an open source local large language model (LLM). I came across the “Awesome-Story-Generation” repository which lists relevant papers describing promising methods like “Re3: Generating Longer Stories With Recursive Reprompting and Revision”, announced in this Twitter thread from October 2022 and “DOC: Improving Long Story Coherence With Detailed Outline Control”, announced in this Twitter thread from December 2022. However, these papers used GPT-3, and I was hoping to find similar techniques implemented with open source tools that I could run locally. If anyone has experience or knows of resources that could help me achieve long, coherent story generation with an open source LLM, I would greatly appreciate any advice or guidance.
This is a cool way to put it, but I think even just errors and randomness in reproduction of source ideas sometimes can count as original ideas. Nevertheless, I also think it doesn’t fully encompass all range of mechanisms by which humans come up with original ideas.
Randomness can give novel combinations, sure, but we shouldn’t call than an original idea.
As for the various ways humans come up with original ideas, they are based on a level of reflection, reasoning and thought processing. We know that’s not possible for an LLM: while they are complex in their details, the way they work is very well defined. They imitate.