用Mamba模型零样本生成短篇故事结尾，助力创作突破瓶颈
Crafting Narrative Closures: Zero-Shot Learning with SSM Mamba for Short Story Ending Generation
- 采用SMM-Mamba模型实现零样本故事续写，无需额外训练
- 在BERTScore、ROUGE等指标上超越GPT-3.5基线模型
- 开源首个面向故事生成的state-space模型，适合创作者与研究者
撰写故事既吸引人又具挑战性，作者常遇创意阻塞。本文提出一种基于提示的自动故事结尾生成工具，输入短篇故事片段即可获得一句或多句自然连贯的结局，帮助克服写作瓶颈并激发灵感。我们使用预训练GPT-3.5与新微调的SSM-Mamba模型构建生成系统，在BERT Score、METEOR、BLEU、ROUGE及困惑度等多项指标上表现优异。所提出的Mamba模型已作为开源模型发布于HuggingFace，是目前首个用于故事生成任务的state-space model，为NLP社区提供重要贡献。
原文摘要 · Abstract (English)
Writing stories is an engaging yet challenging endeavor. Often, authors encounter moments of creative block, where the path forward in their narrative becomes obscured. This paper is designed to address such moments by providing an innovative solution: A tool that completes stories based on given prompts. By inputting a short story prompt, users can receive a conclusion to their story, articulated in one sentence or more, thereby enhancing the storytelling process with AI-driven creativity. This tool aims not only to assist authors in navigating writer's block but also to offer a fun and interactive way for anyone to expand on story ideas spontaneously. Through this paper, we explore the intersection of artificial intelligence and creative writing, pushing the boundaries of how stories can be crafted and concluded. To create our final text-generation models, we used a pre-trained GPT-3.5 model and a newly created finetuned SSM-Mamba model, both of which perform well on a comprehensive list of metrics including BERT score, METEOR, BLEU, ROUGE, and Perplexity. The SSM model has also been made public for the NLP community on HuggingFace models as an open source contribution, which for the timebeing is a first of its kind state-space model for story-generation task on HuggingFace.
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