Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool

Few shot learning with large language models has the potential to give individuals without formal machine learning training the access to a wide range of text to text models. We consider how this applies to creative writers and present Story Centaur, a user interface for prototyping few shot models and a set of recombinable web components that deploy them. Story Centaur’s goal is to expose creative writers to few shot learning with a simple but powerful interface that lets them compose their own co-creation tools that further their own unique artistic directions. We build out several examples of such tools, and in the process probe the boundaries and issues surrounding generation with large language models.

[1]  Mark O. Riedl,et al.  Story Realization: Expanding Plot Events into Sentences , 2020, AAAI.

[2]  Piotr W. Mirowski,et al.  Improvised Theatre Alongside Artificial Intelligences , 2021, AIIDE.

[3]  Noah Wardrip-Fruin,et al.  Cozy mystery construction kit: prototyping toward an AI-assisted collaborative storytelling mystery game , 2019, FDG.

[4]  Ilya Sutskever,et al.  Learning to Generate Reviews and Discovering Sentiment , 2017, ArXiv.

[5]  Ilya Sutskever,et al.  Language Models are Unsupervised Multitask Learners , 2019 .

[6]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[7]  Xing Shi,et al.  Hafez: an Interactive Poetry Generation System , 2017, ACL.

[8]  David C. Uthus,et al.  TextSETTR: Label-Free Text Style Extraction and Tunable Targeted Restyling , 2021, ArXiv.

[9]  Chris Callison-Burch,et al.  Unsupervised Hierarchical Story Infilling , 2019, Proceedings of the First Workshop on Narrative Understanding.

[10]  Luke S. Zettlemoyer,et al.  Deep Contextualized Word Representations , 2018, NAACL.

[11]  Nicky Case,et al.  How To Become A Centaur , 2018 .

[12]  Alec Radford,et al.  Improving Language Understanding by Generative Pre-Training , 2018 .

[13]  Mark O. Riedl,et al.  Improvisational Computational Storytelling in Open Worlds , 2016, ICIDS.

[14]  Simon Colton,et al.  Explainable Computational Creativity , 2020, ICCC.

[15]  Mark Chen,et al.  Language Models are Few-Shot Learners , 2020, NeurIPS.

[16]  Yann Dauphin,et al.  Hierarchical Neural Story Generation , 2018, ACL.

[17]  Emily M. Bender,et al.  Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data , 2020, ACL.

[18]  Piotr W. Mirowski,et al.  Human Improvised Theatre Augmented with Artificial Intelligence , 2019, Creativity & Cognition.

[19]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[20]  Omer Levy,et al.  SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems , 2019, NeurIPS.

[21]  Sungwoo Lee,et al.  I Lead, You Help but Only with Enough Details: Understanding User Experience of Co-Creation with Artificial Intelligence , 2018, CHI.

[22]  Jianfeng Gao,et al.  Towards Coherent and Cohesive Long-form Text Generation , 2018, Proceedings of the First Workshop on Narrative Understanding.

[23]  Emily M. Bender,et al.  On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 , 2021, FAccT.