# What is Wrong with Language Models that Can Not Tell a Story?

arXiv: https://arxiv.org/abs/2211.05044 · HTML: https://mlsanity.com/paper/2211.05044/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 27 of 27)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://mlsanity.com/paper/2204.00598/) (2022)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [QuALITY: Question Answering with Long Input Texts, Yes!](https://mlsanity.com/paper/2112.08608/) (2021)
- [Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://mlsanity.com/paper/2210.06774/) (2022)
- [Pretrained Transformers as Universal Computation Engines](https://mlsanity.com/paper/2103.05247/) (2021)
- [Controlling Linguistic Style Aspects in Neural Language Generation](https://mlsanity.com/paper/1707.02633/) (2017)
- [Strategies for Structuring Story Generation](https://mlsanity.com/paper/1902.01109/) (2019)
- [Content Planning for Neural Story Generation with Aristotelian Rescoring](https://mlsanity.com/paper/2009.09870/) (2020)
- [An Annotated Dataset of Coreference in English Literature](https://mlsanity.com/paper/1912.01140/) (2019)
- [Semantic Exploration from Language Abstractions and Pretrained Representations](https://mlsanity.com/paper/2204.05080/) (2022)
- [SQuALITY: Building a Long-Document Summarization Dataset the Hard Way](https://mlsanity.com/paper/2205.11465/) (2022)
- [Story Realization: Expanding Plot Events into Sentences](https://mlsanity.com/paper/1909.03480/) (2019)
- [Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metric](https://mlsanity.com/paper/2004.05001/) (2020)
- [MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants](https://mlsanity.com/paper/1905.09531/) (2019)
- [METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals](https://mlsanity.com/paper/2204.06644/) (2022)
- [Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers](https://mlsanity.com/paper/2108.00308/) (2021)
- [MPST: A Corpus of Movie Plot Synopses with Tags](https://mlsanity.com/paper/1802.07858/) (2018)
- [InScript: Narrative texts annotated with script information](https://mlsanity.com/paper/1703.05260/) (2017)
- [Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity](https://mlsanity.com/paper/2007.06290/) (2020)
- [StoryDB: Broad Multi-language Narrative Dataset](https://mlsanity.com/paper/2109.14396/) (2021)
- [DYPLODOC: Dynamic Plots for Document Classification](https://mlsanity.com/paper/2107.12226/) (2021)
- [Detecting Everyday Scenarios in Narrative Texts](https://mlsanity.com/paper/1906.04102/) (2019)
- [Rethinking Crowd Sourcing for Semantic Similarity](https://mlsanity.com/paper/2109.11969/) (2021)

## Built on by (most cited first, 4 of 4)

- [Agents' Room: Narrative Generation through Multi-step Collaboration](https://mlsanity.com/paper/2410.02603/) (2024)
- [Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey](https://mlsanity.com/paper/2602.15851/) (2026)
- [Post Turing: Mapping the landscape of LLM Evaluation](https://mlsanity.com/paper/2311.02049/) (2023)
- [Branching Narratives: Character Decision Points Detection](https://mlsanity.com/paper/2405.07282/) (2024)

## Abstract (arXiv)

This paper argues that a deeper understanding of narrative and the successful generation of longer subjectively interesting texts is a vital bottleneck that hinders the progress in modern Natural Language Processing (NLP) and may even be in the whole field of Artificial Intelligence. We demonstrate that there are no adequate datasets, evaluation methods, and even operational concepts that could be used to start working on narrative processing.