Are Large Language Models Capable of Generating Human-Level Narratives?

Yufei Tian, Tenghao Huang, Miri Liu, Derek Jiang, Alexander Spangher, Muhao Chen, Jonathan May, Nanyun Peng


Abstract
As daily reliance on large language models (LLMs) grows, assessing their generation quality is crucial to understanding how they might impact on our communications. This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression. We introduce a novel computational framework to analyze narratives through three discourse-level aspects: i) story arcs, ii) turning points, and iii) affective dimensions, including arousal and valence. By leveraging expert and automatic annotations, we uncover significant discrepancies between the LLM- and human- written stories. While human-written stories are suspenseful, arousing, and diverse in narrative structures, LLM stories are homogeneously positive and lack tension. Next, we measure narrative reasoning skills as a precursor to generative capacities, concluding that most LLMs fall short of human abilities in discourse understanding. Finally, we show that explicit integration of aforementioned discourse features can enhance storytelling, as is demonstrated by over 40% improvement in neural storytelling in terms of diversity, suspense, and arousal. Such advances promise to facilitate greater and more natural roles LLMs in human communication.
Anthology ID:
2024.emnlp-main.978
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
17659–17681
Language:
URL:
https://aclanthology.org/2024.emnlp-main.978
DOI:
10.18653/v1/2024.emnlp-main.978
Bibkey:
Cite (ACL):
Yufei Tian, Tenghao Huang, Miri Liu, Derek Jiang, Alexander Spangher, Muhao Chen, Jonathan May, and Nanyun Peng. 2024. Are Large Language Models Capable of Generating Human-Level Narratives?. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17659–17681, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Are Large Language Models Capable of Generating Human-Level Narratives? (Tian et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-main.978.pdf