Abstract
Automated long-form story generation typically employs long-context large language models (LLMs) for one-shot creation, which can produce cohesive but not necessarily engaging content. We introduce Storytelling With Action Guidance (SWAG), a novel approach to storytelling with LLMs. Our approach reduces story writing to a search problem through a two-model feedback loop: one LLM generates story content, and another auxiliary LLM is used to choose the next best “action” to steer the story’s future direction. Our results show that SWAG can substantially outperform previous end-to-end story generation techniques when evaluated by GPT-4 and through human evaluation. Our SWAG pipeline using only small open-source models surpasses GPT-3.5-Turbo.- Anthology ID:
- 2024.findings-emnlp.824
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 14086–14106
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.824/
- DOI:
- 10.18653/v1/2024.findings-emnlp.824
- Cite (ACL):
- Jonathan Pei, Zeeshan Patel, Karim El-Refai, and Tianle Li. 2024. SWAG: Storytelling With Action Guidance. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 14086–14106, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- SWAG: Storytelling With Action Guidance (Pei et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.findings-emnlp.824.pdf