H o LLM wood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing (2024)

Abstract: Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in natural language processing. In particular, large language models (LLMs) can hardly produce written works at the level of human experts due to the extremely high complexity of literature writing. In this paper, we present HoLLMwood, an automated framework for unleashing the creativity of LLMs and exploring their potential in screenwriting, which is a highly demanding task. Mimicking the human creative process, we assign LLMs to different roles involved in the real-world scenario. In addition to the common practice of treating LLMs as Writer , we also apply LLMs as Editor , who is responsible for providing feedback and revision advice to Writer . Besides, to enrich the characters and deepen the plots, we introduce a role-playing mechanism and adopt LLMs as Actors that can communicate and interact with each other. Evaluations on automatically generated screenplays show that HoLLMwood substantially outperforms strong baselines in terms of coherence, relevance, interestingness and overall quality.

## introduction artistic creation, which has long been regarded as the unique intelligence of human beings, is being redefined by the recent advancements in artificial intelligence. the latest generative models have shown notable creativity, especially in the field of computer vision @xcite . these ai-generated artworks can sometimes achieve a quality indistinguishable from human-created art, blurring the conventional boundaries between human and machine-created arts. however, even those generative models are capable of creating excellent visual artworks on par with those crafted by human experts, the same level of achievements have not yet been observed in the realm of literary creation. screenwriting, the art and craft of writing screenplays, serves as the cornerstone in the production of films, tv series and video games. this highly creative process demands not only deliberate thinking and careful planning, but also an in-depth understanding of human emotions and motivations. despite the recent advances in large language models (llms) @xcite @xcite , it remains extremely challenging for current llms to produce high-quality literary works with simple guidelines. some prior works have explored employing llms for such creative tasks, e.g., story generation @xcite @xcite and co-writing scripts with human experts @xcite , while either dialogues in the story are clearly robotic, or human expertise is required, which comes at a high cost. recent works have explored the potential of solving real-world problems in a multiagent setting with llms @xcite @xcite , while little progress has achieved in creative writing under such setting. this can be attributed to the intricate nature of literary creation. an engaging work requires authors to constantly polish the drafts based on external feedback, step into the shoes of their characters, experience the narrative from their perspectives, and cultivate a deep emotional connection with the audience. therefore, simple guideline may fail in making llms write satisfactory works. in order to explore llms' potential and capability in creative writing, we focus on the aforementioned screenwriting task, since it's close to real life and has promising and valuable applications. we design a fully automated screenwriting framework, named hollmwood, aiming to make llms mimic the creative process of hollywood screenwriters. specifically, we organize llms into different roles involved in the human creative process: writer and editor. editor will provide revision advice for writer on characters and plots. additionally, to bring the characters in the plot to life and flesh them out, we make llms act as actors and develop dialogues and interactions between characters in a role-playing manner. each step of the screenwriting process in our framework is separated from each other, making it easy to introduce human intervention at any stage. we evaluate the generated screenplays via pairwise comparison using gpt-4. the experiments show that the scripts generated with hollm-wood achieve significantly overall better quality compared to strong baselines. we also perform more fine-grained evaluations of different dimensions (coherence, relevance and interestingness). ablations further prove that both feedbackrevision between writer and editor, and roleplaying by actors contribute positively to the final screenplay quality. in summary, our contributions are as follows: ## hollmwood in this section, we present the hollmwood framework that enables llms to create engaging screenplays. the llms will play three main roles in the whole screenwriting process: writer, editor and actors under carefully designed prompts. in the beginning, the writer designs a set of characters and formulates a story's outline (sec. 2.1), grounded in a preliminary storyline. the editor will provide some feedbacks for the writer to polish the initial draft iteratively. then, the subplots within the outline are further elaborated by the writer, each of which is expanded into a full chapter of the story (sec. 2.2). building upon this, the writer drafts a screenplay that includes scenes, characters, actions and conversations, with each tailored to the corresponding chapter (sec. 2.3). to deepen the character performances and elevate the overall interestingness, we introduce a roleplaying mechanism to develop the final screenplay, where the llms are assigned specific characters and prompted to act spontaneously based on the current plot, characters' profile and memory. complete examples of inputs and outputs for hollm-wood's each step are provided in appendix e. ## conclusion in this work, we presented hollmwood, a framework that unleashes the creativity of llms for screenwriting. adopting a feedback-revision and role-playing strategy mirroring the human screenwriting process, our hollmwood is able to significantly improve the capabilities of llms to generate more interesting screenplays. extensive experiments show that screenplays generated with our hollmwood possess a higher degree of coherence, relevance, interestingness, and overall quality, compared to baseline methods.