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

By Benjamin Swanson, Kory Wallace Mathewson, Ben Pietrzak, Sherol Chen, Monica Dinalescu. 2021

85 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Large Language Models, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Fei Wu
2. Richmond Y. Wong
3. Qian Yang
4. J.D. Zamfirescu-Pereira
5. Bjoern Hartmann
6. Carrie J. Cai
7. Jiwei Li
8. Michael Terry

In Computer Vision and Pattern Recognition

1. Qian Yang
2. J.D. Zamfirescu-Pereira
3. Michael Terry
4. Fei Wu
5. Carrie J. Cai
6. Guoyin Wang
7. Xiaofei Sun
8. Tianwei Zhang

In Large Language Models

1. Fei Wu
2. Tianwei Zhang
3. Jiwei Li

In Natural Language Processing

1. Fei Wu
2. Guoyin Wang
3. Jiwei Li
4. Ellen Jiang
5. Michael Terry
6. Carrie J. Cai
7. Xiaofei Sun
8. Xiaoya Li

Citations in the corpus

• PromptChainer: Chaining Large Language Model Prompts through Visual Programming (2022, background): “In the future, it may be worthwhile to further investigate methods that consider inter-dependency between parallel sub-tasks [15].”
• GPT-NER: Named Entity Recognition via Large Language Models (2023, background): “, 2022) have obtained significant performance boosts on a variety of natural language processing tasks (Hegselmann et al., 2022; Vilar et al., 2022; Perez et al., 2021; Pietrzak et al., 2021; Wei et al., 2021).”
• Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts (2023, background): “Here, we briefy overview known barriers and challenges in end-user programming and iML (list below [27]) as well as how experts and non-experts approach these tasks diferently (Table 2), to motivate and contextualize our investigation into end-users’ intuitive approaches to prompt design.”