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[2602.15851] Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey
Summary
This survey explores the intersection of narrative theory and large language models (LLMs) for automatic story generation and understanding, proposing a taxonomy and highlighting methodological trends.
Why It Matters
Understanding how narrative theories can enhance LLM applications is crucial for advancing both natural language processing and narrative studies. This survey provides a foundational overview that can guide future interdisciplinary research and improve model performance in narrative tasks.
Key Takeaways
- The survey proposes a taxonomy for narrative studies within NLP.
- It identifies challenges in defining narrative quality benchmarks.
- Future research should focus on theory-based metrics for narrative attributes.
- Interdisciplinary collaboration can enhance narrative understanding in AI.
- LLMs can bridge NLP pipelines with abstract narrative concepts.
Computer Science > Computation and Language arXiv:2602.15851 (cs) [Submitted on 23 Jan 2026] Title:Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey Authors:David Y. Liu, Aditya Joshi, Paul Dawson View a PDF of the paper titled Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey, by David Y. Liu and 1 other authors View PDF HTML (experimental) Abstract:Applications of narrative theories using large language models (LLMs) deliver promising use-cases in automatic story generation and understanding tasks. Our survey examines how natural language processing (NLP) research engages with fields of narrative studies, and proposes a taxonomy for ongoing efforts that reflect established distinctions in narratology. We discover patterns in the following: narrative datasets and tasks, narrative theories and NLP pipeline and methodological trends in prompting and fine-tuning. We highlight how LLMs enable easy connections of NLP pipelines with abstract narrative concepts and opportunities for interdisciplinary collaboration. Challenges remain in attempts to work towards any unified definition or benchmark of narrative related tasks, making model comparison difficult. For future directions, instead of the pursuit of a single, generalised benchmark for 'narrative quality', we believe that progress benefits more from efforts that focus on the following: defining and improving theory-based metrics for in...
