Re3: Generating Longer Stories With Recursive Reprompting and Revision (2022.emnlp-main)
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| Challenge: | Recent work has generated short stories of several pages in length, but they are much shorter than typical short stories meant for human consumption. |
| Approach: | They propose a framework to generate long-range plot coherence and relevance by prompting a general-purpose language model and a language model. |
| Outcome: | The proposed framework generates stories of 2000-2500 words, compared to similar-length stories generated directly from the same model. |
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Text-to-Text Automatic Story Generation: A Survey (2026.eacl-srw)
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| Challenge: | Automated story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement . despite advances in large language models, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. |
| Approach: | They propose to develop new evaluation metrics and better data sets to support automatic story generation. |
| Outcome: | The proposed evaluation metrics and better datasets will improve narrative coherence and consistency and explore practical applications of story generation. |
Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding (2024.acl-long)
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| Challenge: | Generating long-term texts using artificial intelligence has always been a challenge . however, the generated novels exhibit poor logical coherence and appeal in their plots and deficiencies in character and event depiction, ultimately compromising the overall narrative quality. |
| Approach: | They propose a method for extracting excelsior and expanding from novel data to generate arbitrarily long novels using large language models. |
| Outcome: | The proposed method produces high-quality long-form novels with a high level of logical coherence and appeal despite the use of large language models. |
Strategies for Structuring Story Generation (P19-1)
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| Challenge: | Existing language models generate word by word, but fail to capture high-level interactions . a novel decomposition approach allows more abstract representations to be generated first . |
| Approach: | They propose models which abstract over actions and entities to create stories . they generate predicate-argument structure, then replace placeholders with context-sensitive names . |
| Outcome: | The proposed models improve diversity and coherence of events and entities in generated stories. |
Returning to the Start: Generating Narratives with Related Endpoints (2024.naacl-short)
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| Challenge: | RENarGen generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
| Approach: | They propose a novel novel novel that generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
| Outcome: | The proposed paradigm generates closed narratives by ensuring the first and last sentences are related and then infilling the middle sentences. |
Content Planning for Neural Story Generation with Aristotelian Rescoring (2020.emnlp-main)
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| Challenge: | Current approaches to narrative composition are plagued by difficulty in mastering structure, will veer between topics, and lack long-range cohesion. |
| Approach: | They propose a plot-generation language model and a set of rescoring models that implement an aspect of good story-writing as detailed in Aristotle's Poetics. |
| Outcome: | The proposed system improves the quality of the narrative generated from the proposed model and improves its relevance to a given prompt and quality of stories written with our principled plot structure. |
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning (2022.findings-emnlp)
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| Challenge: | Existing methods to automate story generation focus on single-character stories and lack basiccommonsense reasoning. |
| Approach: | They propose a commonsense-inference Augmentedneural StoryTelling framework that introduces commonsensical reasoning into the story generation process. |
| Outcome: | The proposed method produces significantly more coherent, on-topic, enjoyable andfluent stories than existing models in both the single-character and two-character settings. |
What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation (2026.acl-long)
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Shaomu Tan, Dawei Zhu, Ke Tran, Michael Denkowski, Sony Trenous, Leonardo F. R. Ribeiro, Bill Byrne, Felix Hieber
| Challenge: | Large language models (LLMs) have made document-level machine translation increasingly practical, enabled by long-context modeling and strong generation quality. |
| Approach: | They propose to use document-level MT followed by segment-level refinement to find the strongest and most stable improvements across six LLMs and seven language pairs. |
| Outcome: | The proposed method outperforms error-specific prompting and evaluate-then-refine schemes in document-level translation. |
A Survey on LLMs for Story Generation (2025.findings-emnlp)
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Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee
| Challenge: | Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. |
| Approach: | They propose a novel taxonomy of LLMs for story generation consisting of two major paradigms: independent story generation by an LLM, and author-assistance for story creation . |
| Outcome: | The proposed taxonomy compares existing work on the topic with those of novel author-assistance models. |
Hierarchical Neural Story Generation (P18-1)
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| Challenge: | a hierarchical model that generates a premise and then conditions on it creates fluent text . a novel form of model fusion improves the relevance of the story to the prompt . |
| Approach: | They use a hierarchical model that first generates a premise, then transforms it into a text . they use fusion to improve relevance of the story to the prompt and add a gated mechanism to model context . |
| Outcome: | The proposed model improves on strong baselines on automated and human evaluations. |
COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion (2021.acl-long)
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| Challenge: | Existing inference models are opaque, but they can be made more interpretable by explicitly generating interim inference rules and using them to guide the generation of task-specific textual outputs. |
| Approach: | They propose a recursive inference framework that iteratively reads context sentences and dynamically generates contextualized inference rules, encodes them, and uses them to guide output generation. |
| Outcome: | The proposed framework generates better story sentences than baseline models, and is more interpretable than existing models. |