Crafting Narrative Closures: Zero-Shot Learning with SSM Mamba for Short Story Ending Generation

TL;DR

This paper introduces a novel AI tool that generates story endings from prompts using a fine-tuned SSM-Mamba model and GPT-3.5, aiming to assist creative writers and explore AI-driven storytelling.

Contribution

It presents a new state-space model for story generation, fine-tuned for narrative closure, and provides an open-source implementation on HuggingFace.

Findings

The SSM-Mamba model performs well on multiple NLP metrics.

The tool effectively helps overcome writer's block.

The model is publicly available for community use.

Abstract

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.