A Survey on LLM s for Story Generation (2025)

Abstract: Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. We create a novel taxonomy of LLMs for story generation consisting of two major paradigms: (i) independent story generation by an LLM, and (ii) author-assistance for story generation – a collaborative approach with LLMs supporting human authors. We compare existing works based on their methodology, datasets, generated story types, evaluation methods, and LLM usage. With a comprehensive survey, we identify potential directions for future work

## introduction highly capable llms like chatgpt, llama, and more @xcite open up possibilities to rethink and re-formulate the static, existing ways of storytelling @xcite . for example, with llms, stories can be interactive @xcite and personalized @xcite , responding flexibly to users in real time. these new ways of storytelling create significant economic opportunities, for example: improving player experiences in the gaming industry @xcite , improving childcare quality and training health professionals in the healthcare industry @xcite , improving teaching methods in education @xcite , and improving movie script development in the entertainment industry @xcite . despite the appealing capabilities of llms, llm outputs often suffer from hallucinations, factual inaccuracies, and the generation of offensive content. furthermore, existing models may be incapable of conceptualizing key story arcs and understanding and interpreting nuanced human emotions. for example, @xcite finds that llms are not able to correctly summarize key aspects of stories, including the story subtext and the (un)reliability of story narrators. while there is a vast literature of work on digital storytelling @xcite @xcite and the use of traditional language models -i.e., pre-llm foot_0 -for story generation @xcite , there is a gap in the literature in surveying the use of llms in story generation. the closest prior work, @xcite , surveys storytelling for data interpretation applications, whereas we focus our work on storytelling in the traditional sense (i.e., non-data-centric storytelling), spanning application areas from education to the interactive video game story generation. specifically, we survey recent early-stage works using llms 1 for story generation to close the gap. we provide a systematic understanding of the area to highlight the opportunities for follow-up work. we bridge the gap between hci-style story systems and nlp-style story systems, ideating future work including: the creation of large-scale datasets and metrics, the use of open-sourced and small models, and the use of inference-time methods for effectively controlling llms @xcite . we make the following contributions: ## comparison of datasets we compare the datasets used in the highlighted story generation systems, as detailed in several systems rely on established story text datasets -such as the writing prompts dataset (collabstory) and story commonsense dataset (mld-ea) -to provide structured narrative inputs or benchmarks for evaluation. these datasets allow for reproducible experiments and comparative assessments. notably, a significant number of systems (osos, storyverse, charactermeet, meat, etc.) operate without formal datasets, relying instead on user input or synthetic prompts during user studies. while this supports personalization and real-world interactivity, it limits standardization and reproducibility. the lack of shared, diverse datasets tailored for interactive and adaptive storytelling is a major gap in current research. expanding and standardizing datasets -especially those that integrate narrative structure, emotion, user feedback, and visual components -would greatly enhance the comparability, scalability, and realism of llm-based storytelling systems. ## comparison of llm use we compare the types of llms and their uses for story generation, as detailed in while gpt-4 and its variants dominate higherend use cases, a number of systems (e.g., osos, collabstory, swag) demonstrate the growing capabilities of open-source models such as llama, gemma, and mistral. these systems increasingly experiment with hybrid or multi-agent setups to simulate creativity (collabstory) or improve narrative coherence via iterative refinement (swag). ## discussion & future directions in this section, we address some limitations of current llm-based models in story writing and propose several potential directions for future work. additional considerations are provided in appendix b. opportunities for multimodal storytelling. recent advancement in vision-language models (vlms) provides unique opportunities for generating multimodal stories. one of the key challenges is generating a sequence of coherent, contextually relevant images and texts. many recent works @xcite @xcite have focused on addressing this challenge. seed-story @xcite leverages multimodal large language model (mllm) to generate a sequence of rich and coherent narrative texts, along with images that share consistent characters and styles, given user-provided images and text as the beginning of the story. later work story-llava @xcite exploits llava @xcite for generating more engaging and human-preferred narratives. in addition, intelligent grimm @xcite ) focused on open-ended storytelling by leveraging a visual-language module and a pre-trained stable diffusion model to generate unseen characters with coherent visual stories that are aligned to a given storyline.