Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey
arXiv:2602.15851v2 Announce Type: replace-cross Abstract: Applications of narrative theories using large language models (LLMs) deliver promising methods in automatic story generation and understanding tasks. Our survey examines how natural language processing (NLP) research uses LLM methods to engage with diverse concepts from narrative studies. We use established distinctions from narratology to categorise ongoing efforts and discover the following: \redtext{(a) narrative texts come from diverse sources beyond just literature, (b) theoretical synthesis and validation are potential outcomes,
The rapid advancement and widespread adoption of large language models (LLMs) necessitate the development of more sophisticated methods for content generation and understanding, particularly in narrative contexts.
This development indicates a maturation in AI's ability to engage with complex human constructs like storytelling, moving beyond basic text generation to theoretical application, which is critical for future human-AI interaction and content creation.
The explicit integration of narrative theory into LLM development shifts the paradigm from purely data-driven approaches to more conceptually informed AI, enabling more nuanced and coherent storytelling capabilities.
- · Content creators and studios
- · AI-powered entertainment platforms
- · Human-computer interaction researchers
- · NLP researchers
- · Generative AI models lacking theoretical grounding
- · Platforms relying solely on statistical language models
Improved automatic story generation and understanding leading to more compelling AI-generated narratives.
The development of new AI applications in sectors like education, therapy, and creative arts based on advanced narrative capabilities.
Potential blurring of lines between human-authored and AI-authored content, impacting intellectual property and creative industries.
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Read at arXiv cs.AI