Unmet Creativity Support Needs in Computationally Supported Creative Writing (2022)

Abstract: Large language models (LLMs) enabled by the datasets and computing power of the last decade have recently gained popularity for their capacity to generate plausible natural language text from human-provided prompts. This ability makes them appealing to fiction writers as prospective co-creative agents, addressing the common challenge of writer’s block, or getting unstuck. However, creative writers face additional challenges, including maintaining narrative consistency, developing plot structure, architecting reader experience, and refining their expressive intent, which are not well-addressed by current LLM-backed tools. In this paper, we define these needs by grounding them in cognitive and theoretical literature, then survey previous computational narrative research that holds promise for supporting each of them in a co-creative setting.

## introduction mixed-initiative co-creative @xcite creativity support tools @xcite for creative writing have recently seen a surge of interest in research communities, coinciding with the introduction of large language models (llms) such as gpt-3 @xcite that can provide coherent suggestions for the continuation of human-written text. several recent efforts have been made to understand the experiences of writers who work with these tools to produce texts @xcite @xcite . however, less attention has been paid to the development of systems that can provide forms of creative writing support beyond short-term suggestions for textual continuation. meanwhile, recent efforts to understand the playful creative writing communities that have emerged around interactive emergent narrative games @xcite and to provide computational support for playful creative writing at the plotstructure level @xcite have revealed a preliminary inventory of several distinct but interrelated creativity support needs among creative writers, including: current large language models are good at addressing the first of these needs, getting unstuck, via short-term suggestions that can prompt writers to take their stories in unexpected new directions. however, they do not directly address consistency maintenance, longer-term plot structure, management of reader experience, or the challenge of refining high-level expressive intent, and some novelists even suggest that llms may actively work against the construction of coherent plot structure due to the highly divergent nature of llm suggestions @xcite . some recent work aims to improve llms in ways that could enable them to meet these needs: for instance, work in long text generation @xcite @xcite could assist users with consistency maintenance; work on hierarchical concept-driven language models @xcite could help to maintain plot structure in generated text; and work in diverse decoding methods @xcite could help users refine their intent by selecting from among diverse potential completions of the same text. however, the possibility of supporting these needs through other forms of technology may also be worth investigating. in this paper, we describe each of these creative writing support needs in more detail, then survey previous research from communities outside of nlp/computational linguistics that have either been shown capable of addressing, or that show potential for supporting these creative needs. our aim with this paper is to create a bridge between the acl community and ai/digital games research community that may yield productive insight towards synthesizing these approaches that have evolved in parallel. we limit the scope of our discussion primarily to narrative fiction, particularly in the form of short stories, novels, and game writing/interactive storytelling, so the suggestions made here may not all be applicable to other forms of creative writing (such as poetry). however, we attempt to avoid limiting ourselves to purely text-based storytelling in which only the written word is used to convey meaning; we are also interested in forms of narrative fiction that target visual, audio, and hybrid renderings of fictional events, such as film and game narrative, since many technologies capable of reasoning about plot structure are readily applicable to these domains. ## technologies and approaches in this section, we overview technologies that have shown promise for addressing the needs outlined in the previous section. ## conclusion we have presented five creative writing support needs, only one of which (getting unstuck) is meaningfully supported by current large language models, and surveyed technologies for addressing the remaining four needs that have arisen from the ai/digital games research community. these technologies are at varying levels of maturity, and most of them have only been tested in purely automated or generative forms rather than in mixed-initiative, co-creative interaction modes. an important line of future work will be to evaluate these technologies in those modes and determine interfaces and interaction protocols that amplify and foster human creativity in the writing process. our goal with this paper is not to assert the superiority of world-model or knowledge-engineering based approaches over llms, but rather to emphasize that there is a set of needs and affordances that these techniques can address and provide that are complementary to the needs addressed and affordances provided by llms. by bridging research communities focused (on one hand) on computing with natural language and (on the other) on simulating story worlds and reasoning about narrative structure, we hope to pave the way for hybrid and unified models that can transform the human creative writing experience-much like the neurosymbolic approaches to automated story generation (martin, 2021) that undergird several recent advances in story generation as a field.