# End-to-end Story Plot Generator arXiv: https://arxiv.org/abs/2310.08796 · HTML: https://mlsanity.com/paper/2310.08796/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 41 of its 54 references that are in the map. - First posted on arXiv 13 Oct 2023. ## Builds on (most cited first, 30 of 41) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023) - [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022) - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021) - [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023) - [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020) - [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020) - [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019) - [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021) - [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018) - [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation](https://mlsanity.com/paper/2108.12409/) (2021) - [Extending Context Window of Large Language Models via Positional Interpolation](https://mlsanity.com/paper/2306.15595/) (2023) - [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023) - [FUDGE: Controlled Text Generation With Future Discriminators](https://mlsanity.com/paper/2104.05218/) (2021) - [Autoformalization with Large Language Models](https://mlsanity.com/paper/2205.12615/) (2022) - [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022) - [LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models](https://mlsanity.com/paper/2309.12307/) (2023) - [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018) - [Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://mlsanity.com/paper/2210.06774/) (2022) - [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022) - [Transformer Language Models without Positional Encodings Still Learn Positional Information](https://mlsanity.com/paper/2203.16634/) (2022) - [Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections](https://mlsanity.com/paper/2104.04670/) (2021) - [A Length-Extrapolatable Transformer](https://mlsanity.com/paper/2212.10554/) (2022) - [Strategies for Structuring Story Generation](https://mlsanity.com/paper/1902.01109/) (2019) - [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020) - [Dialogue State Tracking with a Language Model using Schema-Driven Prompting](https://mlsanity.com/paper/2109.07506/) (2021) - [Content Planning for Neural Story Generation with Aristotelian Rescoring](https://mlsanity.com/paper/2009.09870/) (2020) - [Counterfactual Story Reasoning and Generation](https://mlsanity.com/paper/1909.04076/) (2019) ## Built on by (most cited first, 7 of 7) - [MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation](https://mlsanity.com/paper/2406.05690/) (2024) - [SWAG: Storytelling With Action Guidance](https://mlsanity.com/paper/2402.03483/) (2024) - [LCFO: Long Context and Long Form Output Dataset and Benchmarking](https://mlsanity.com/paper/2412.08268/) (2024) - [WHAT-IF: Exploring Branching Narratives by Meta-Prompting Large Language Models](https://mlsanity.com/paper/2412.10582/) (2024) - [PREFINE: Personalized Story Generation via Simulated User Critics and User-Specific Rubric Generation](https://mlsanity.com/paper/2510.21721/) (2025) - [Beyond Direct Generation: A Decomposed Approach to Well-Crafted Screenwriting with LLMs](https://mlsanity.com/paper/2510.23163/) (2025) - [StorySpark: Module-wise Evolutionary Search for Story Premise Generation](https://mlsanity.com/paper/2608.12336/) (2026) ## Abstract (arXiv) Story plots, while short, carry most of the essential information of a full story that may contain tens of thousands of words. We study the problem of automatic generation of story plots, which includes story premise, character descriptions, plot outlines, etc. To generate a single engaging plot, existing plot generators (e.g., DOC (Yang et al., 2022a)) require hundreds to thousands of calls to LLMs (e.g., OpenAI API) in the planning stage of the story plot, which is costly and takes at least several minutes. Moreover, the hard-wired nature of the method makes the pipeline non-differentiable, blocking fast specialization and personalization of the plot generator. In this paper, we propose three models, textttOpenPlot, textttE2EPlot and textttRLPlot, to address these challenges. textttOpenPlot replaces expensive OpenAI API calls with LLaMA2 (Touvron et al., 2023) calls via careful prompt designs, which leads to inexpensive generation of high-quality training datasets of story plots. We then train an end-to-end story plot generator, textttE2EPlot, by supervised fine-tuning (SFT) using approximately 13000 story plots generated by textttOpenPlot. textttE2EPlot generates story plots of comparable quality to textttOpenPlot, and is > 10× faster (1k tokens in only 30 seconds on average). Finally, we obtain textttRLPlot that is further fine-tuned with RLHF on several different reward models for different aspects of story quality, which yields 60.0% winning rate against textttE2EPlot along the aspect of suspense and surprise.