While pre-trained language models can generate individually fluent sentences
for automatic story generation, they struggle to generate stories that are
coherent, sensible and interesting. Current state-of-the-art (SOTA) story
generation models explore using higher-level features such as plots or
commonsense knowledge to improve the quality of generated stories. Prompt-based
learning using very large pre-trained language models (VLPLMs) such as GPT3 has
demonstrated impressive performance even across various NLP tasks. In this
paper, we present an extensive study using automatic and human evaluation to
compare the story generation capability of VLPLMs to those SOTA models in three
different datasets where stories differ in style, register and length. Our
results show that VLPLMs generate much higher quality stories than other story
generation models, and to a certain extent rival human authors, although
preliminary investigation also reveals that they tend to ``plagiarise'' real
stories in scenarios that involve world knowledge.

本文对使用 VLPLMs 和 SOTA 模型在三个不同的数据集上进行的故事生成能力进行了自动和人为评估，结果表明 VLPLMs 生成的故事比其他故事生成模型更高质量，有一定程度上的过人之处，但也揭示了在涉及世界知识的情况下容易 “抄袭” 真实故事的现象。