139 - Coherent Long Story Generation, with Kevin Yang

How can we generate coherent long stories from language models?
Ensuring that the generated story has long range consistency and
that it conforms to a high level plan is typically challenging. In
this episode, Kevin Yang describes their system that prompts
language models to first generate an outline, and iteratively
generate the story while following the outline and reranking and
editing the outputs for coherence. We also discussed the challenges
involved in evaluating long generated texts. Kevin Yang is a PhD
student at UC Berkeley. Kevin's webpage:
https://people.eecs.berkeley.edu/~yangk/ Papers discussed in this
episode: 1. Re3: Generating Longer Stories With Recursive
Reprompting and Revision
(https://www.semanticscholar.org/paper/Re3%3A-Generating-Longer-Stories-With-Recursive-and-Yang-Peng/2aab6ca1a8dae3f3db6d248231ac3fa4e222b30a)
2. DOC: Improving Long Story Coherence With Detailed Outline
Control
(https://www.semanticscholar.org/paper/DOC%3A-Improving-Long-Story-Coherence-With-Detailed-Yang-Klein/ef6c768f23f86c4aa59f7e859ca6ffc1392966ca)