Collaborative Storytelling with Large-scale Neural Language Models

By Eric Nichols, Leo Gao, Randy Gomez. 2020

61 citations recorded by the source corpus.

Storytelling plays a central role in human socializing and entertainment. However, much of the research on automatic storytelling generation assumes that stories will be generated by an agent without any human interaction. In this paper, we introduce the task of collaborative storytelling, where an artificial intelligence agent and a person collaborate to create a unique story by taking turns adding to it. We present a collaborative storytelling system which works with a human storyteller to create a story by generating new utterances based on the story so far. We constructed the storytelling system by tuning a publicly-available large scale language model on a dataset of writing prompts and their accompanying fictional works. We identify generating sufficiently human-like utterances to be an important technical issue and propose a sample-and-rank approach to improve utterance quality. Quantitative evaluation shows that our approach outperforms a baseline, and we present qualitative evaluation of our system’s capabilities.

Subfields: Natural Language Processing, Generative Models

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Citations in the corpus

Training Verifiers to Solve Math Word Problems (2021, background): “Nichols et al. (2020) proposed a sample-and-rank approach to improve the collaborative storytelling ability of large language models, with the training signal coming from the preferences of human workers.”
Let's Verify Step by Step (2023, background): “, 2022) or to perform search via rejection sampling (Nichols et al., 2020; Shen et al., 2021; Cobbe et al., 2021).”
Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry Professionals (2022, background): “Recent work addressed human-AI text revision [31], text summarization [18, 123] and overcoming the writer’s block [22, 42, 77, 125].”
Wordcraft: Story Writing With Large Language Models (2022, background): “…English: afternoon French: de l’après-midi English: evening language model: French: le soir Despite the simplicity of this API, large language models (LLMs) have demonstrated proficiency at a wide variety of tasks—from storytelling [1, 28] to code synthesis [4, 9] and email auto-completion [40].”
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (2023, unknown): “Figure 4 illustrates that the evolution process does not yield higher similarity scores.”
Cut the CARP: Fishing for zero-shot story evaluation (2021, background): “Recent breakthroughs in natural language processing (NLP) and natural language generation (NLG) have revitalized interest in applying computational methods to story (See et al., 2019; Xu et al., 2020; Nichols et al., 2020; Fang et al., 2021; Hazarika et al., 2021).”