GROVE : A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence (2023)

Abstract: Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on multiple NLP tasks, including story generation, it is challenging to generate stories with both complex and creative plots. Existing methods often rely on detailed prompts to guide LLMs to meet target conditions, which inadvertently restrict the creative potential of the generated stories. We argue that leveraging information from exemplary human-written stories facilitates generating more diverse plotlines. Delving deeper into story details helps build complex and credible plots. In this paper, we propose a retrieval-auGmented stoRy generation framework with a fOrest of eVidEnce (GROVE) to enhance stories’ complexity. We build a retrieval repository for target conditions to produce few-shot examples to prompt LLMs. Additionally, we design an “asking-why” prompting scheme that extracts a forest of evidence, providing compensation for the ambiguities that may occur in the generated story. This iterative process uncovers underlying story backgrounds. Finally, we select the most fitting chains of evidence from the evidence forest and integrate them into the generated story, thereby enhancing the narrative’s complexity and credibility. Experimental results and numerous examples verify the effectiveness of our method.

## introduction conditional automatic storytelling, generating a story that satisfies specific target conditions, has gained significant attention in the natural language processing community @xcite . generating stories with complex plots is particularly crucial as it creates engaging stories of human-level quality for various applications, such as ai novelists and ai playwrights @xcite . story generation is an active research area where existing studies approach it from two directions: enhancing controllability and incorporating commonsense knowledge @xcite . to satisfy target constraints, researchers enhance the controllability of generation models @xcite . @xcite follow an outline of the plots to generate stories. @xcite propose a bart-based @xcite model to generate stories according to the fine-grained personalized guidance. additionally, to produce fluent and coherent storylines, researchers investigate incorporating commonsense knowledge into generation @xcite @xcite . @xcite introduce commonsense inference into @xcite model to improve narritive coherence. @xcite combine knowledge retrieval, knowledge selection, and story generation together to make the generated story more reasonable. the above studies focus on improving controllability and logical coherence but rarely explore the generation of stories with complex plots. large language models (llms) learn commonsense knowledge from massive texts and develop strong abilities to follow human instructions @xcite @xcite . thus, llm-based prompt learning generates fluent and coherent stories with high controllability @xcite @xcite . @xcite prompt @xcite with combinations of multiple target conditions. @xcite demonstrate that by using prompts, gpt-3 generates higherquality stories than other state-of-the-art (sota) models. typically, llms generate stories based on a given prompt (i.e. text spans or a few sentences) and the outputs are continuations of the given texts. however, a recurring issue emerges in the llm-based prompting approaches to generate complex stories: there is a struggle to balance the complexity and creativity within the generated stories @xcite . to prompt llms to generate stories with complex plots, users often need to detail control signals within the prompt. this approach presents a dilemma: the more control information provided, the more likely it is that the generated story will focus solely on describing the given content, thus constraining the story's potential creativity. we argue that leveraging the information (e.g. story background and plots) from exemplary human stories facilitates generating more diverse plots. delving into story details enriches the narrative with the necessary information, thereby helping to build complex and credible storylines. in this paper, we propose a retrieval-augmented complex story generation framework with a forest of evidence (grove), which leverages existing stories and evidence to generate and rewrite stories for more complex plots. we construct a retrieval repository that enables the llm to learn diverse plots and common patterns from human-written stories. this assists the llm in generating stories with complex plots. moreover, we design an "asking-why" foot_0 prompting scheme that iteratively builds an evidence forest addressing the ambiguities found in the story from various perspectives. the evidence forest refers to a collection or set of evidence trees that are generated to supplement a story in grove. each evidence tree consists of nodes representing pieces of evidence and edges connecting them. the root node of the tree represents an ambiguous or unclear part in the generated story, while the non-root nodes represent additional information that provides clarity and background details to the nodes above them in the tree. finally, we select the optimal chains of evidence from the evidence forest and integrate them into the generated story, thereby enhancing its complexity and credibility. our method is not intended to replace any specifically designed prompts or techniques currently employed in the field. instead, we propose a flexible and generalizable framework that enables llms to generate stories with complex plots, complementing existing methods. our contributions are threefold: 1) we develop a retrieval-augmented framework for generating stories with complex plots by prompting an llm; 2) we introduce an "asking-why" prompting scheme to generate a forest of evidence and rewrite the original story based on the optimal evidence chains; 3) our approach achieves sota performance on quantities of testing cases. detailed analyses validate the effectiveness of our approach. ## conclusion we propose grove, a retrieval-augmented framework to generate stories with complex plots by iter-6 app.copyleaks.com ative asking-why prompting. we build a retrieval repository with existing stories by extracting desired controlling information to inspire a new generation. we design an algorithm that iteratively prompts llm to obtain a forest of evidence that compensates for the generated story. moreover, we revise the story by referring to the most relevant chains of evidence. experimental results show that our proposed method outperforms strong baselines in ensuring story complexity and creativity.