@inproceedings{pei-etal-2024-swag,
title = "{SWAG}: Storytelling With Action Guidance",
author = "Pei, Jonathan and
Patel, Zeeshan and
El-Refai, Karim and
Li, Tianle",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.824",
pages = "14086--14106",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SWAG: Storytelling With Action Guidance
%A Pei, Jonathan
%A Patel, Zeeshan
%A El-Refai, Karim
%A Li, Tianle
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F pei-etal-2024-swag
%X 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.
%U https://aclanthology.org/2024.findings-emnlp.824
%P 14086-14106
Markdown (Informal)
[SWAG: Storytelling With Action Guidance](https://aclanthology.org/2024.findings-emnlp.824) (Pei et al., Findings 2024)
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.