Story-Yarn : An Interactive Story Building Application (2024)

Abstract: Story building is an important part of language and overall development of a child. Developing an interactive and artificial intelligence (AI) based solution to create stories for children is an open and challenging problem. Methods combining large language models (LLMs) and knowledge graphs (KGs) have further enabled high quality and coherent story generation. In this work, we present a platform, Story Yarn, developed for interactive story creation for children. We customise a KG, using children stories, which captures relationships between components of stories. This customised KG is then used along with LLM to collaboratively create a story. We have also built a simple app to facilitate user interaction. This platform can aid the creative development of children, and can be used at home or in schools.

## introduction storytelling is an important part of human society. stories help in gaining and sharing knowledge in a memorable way and developing emotional connections. they are an important part of a child's development. while children are generally very creative, one can imagine ways to better channelize and support this creativity and at the same time help develop their linguistic skills. playand-learn systems that help in interactive storybuilding can serve this purpose. interactive storytelling, a collaborative approach, encourages the co-creators of a story to make decisions and give a desired direction to the story. this engagement makes co-creators active participants rather than being passive observers. apart from traditional interactive story building methods such as "story cubes/wheels" and "prompts in a jar", digital storytelling, using ai and non-ai methods, is gaining significance in language learning in children @xcite . the methods which are not based on ai, do not provide assistance in text creation part. whereas, the contemporary llms @xcite @xcite , which come under the umbrella of generative ai, have facilitated the text creation component in story generation process. these models have humongous learnable parameters and are trained on huge corpus of data available on the internet, enabling them learn, and hence generate, semantically and grammatically correct text information. llms generate text when they are given a context in the form of a prompt. directly using llms for interactive generation is a cumbersome process, as a user has to give specific prompt for every interactive generation. there are many other shortcomings of directly using llms for story generation @xcite , lack of domain knowledge is one of them. when evolving a story, in addition to causal connections in a story, the creative aspect of human mind looks for various new directions. whereas, llms look backward at the story to provide directions in which stories can go. hence, their choices have limited creativity and tend to get predictable. also, given guidance in the form of specific external domain knowledge, the generation quality of language models have shown improvement in performance @xcite . this external knowledge is often stored in the form of structured graphs that follow a defined schema or topology @xcite @xcite . based on this, in our work we have created a kg using stories and have combined it with llm gemini (google, 2024) for interactive story generation. here, the role of our customised kg is to assist the llm in building more creative and diverse stories. we have used various evaluation methods to verify the effectiveness of using kg and llm for interactive story generation. many digital story building platforms are developed based on both ai @xcite @xcite and non-ai @xcite @xcite technologies. interaction with these tools is often challenging for young children. hence, we developed a simple interactive platform, story yarn, which gives the users future path options for story progression. summary of contributions: our proposed platform, story yarn, for interactive story creation involves: the paper is organized as follows: section 2, describes prior work related to story generation and more specifically to methods of interactive generation and usage of kgs in this area. the methods for creating kg and an interactive story generation framework proposed in this work are discussed in section 3. the evaluation of our proposed framework and results are presented in section 4, with relevant conclusions and future directions discussed in section 5. ## related work in this section, we present a brief review of recent interactive story generation methods and existing story generation platforms for children. ## method in this section, we discuss in detail the proposed story generation method, story yarn, which has interactive communication between a user and a framework consisting of llm and our customized kg. we begin with the construction of our kg followed by elaborating on our framework. ## experiments and results in this section, we present a detailed evaluation of our kg and story-creation platform, story yarn, including the dataset, the experimental settings, and their results. ## conclusion and future work in this paper, we have reported a complete platform for interactive story creation for young children using customised story knowledge graph and large language model. the effectiveness of our story knowledge graph further aids the llm to generate coherent and diverse future story paths for evolving story. in future, we plan to add voice and image assistance to the existing work. it would also be interesting to explore adding more stories and fusing the story graph with commonsense graphs.