Story Generation
Story generation research aims to create artificial intelligence systems capable of producing coherent, engaging, and creative narratives. Current efforts focus on improving narrative coherence and creativity using large language models (LLMs), often enhanced by techniques like multi-agent collaboration, iterative refinement, and retrieval-augmented generation, sometimes incorporating multimodal elements such as images. This field is significant for advancing AI capabilities in natural language processing and has potential applications in entertainment, education, and assistive technologies.
Papers
A Continuum of Generation Tasks for Investigating Length Bias and Degenerate Repetition
Darcey Riley, David Chiang
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics
Henglin Huang, Chen Tang, Tyler Loakman, Frank Guerin, Chenghua Lin
NGEP: A Graph-based Event Planning Framework for Story Generation
Chen Tang, Zhihao Zhang, Tyler Loakman, Chenghua Lin, Frank Guerin