EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form
  Narrative Text Generation
Plan-and-Write is a common hierarchical approach in long-form narrative text
generation, which first creates a plan to guide the narrative writing.
Following this approach, several studies rely on simply prompting large
language models for planning, which often yields suboptimal results. In this
paper, we propose a new framework called Evaluation-guided Iterative Plan
Extraction for long-form narrative text generation (EIPE-text), which extracts
plans from the corpus of narratives and utilizes the extracted plans to
construct a better planner. EIPE-text has three stages: plan extraction,
learning, and inference. In the plan extraction stage, it iteratively extracts
and improves plans from the narrative corpus and constructs a plan corpus. We
propose a question answer (QA) based evaluation mechanism to automatically
evaluate the plans and generate detailed plan refinement instructions to guide
the iterative improvement. In the learning stage, we build a better planner by
fine-tuning with the plan corpus or in-context learning with examples in the
plan corpus. Finally, we leverage a hierarchical approach to generate long-form
narratives. We evaluate the effectiveness of EIPE-text in the domains of novels
and storytelling. Both GPT-4-based evaluations and human evaluations
demonstrate that our method can generate more coherent and relevant long-form
narratives. Our code will be released in the future.
6528981f94bb032403d407df
Wang You
6528981f94bb032403d407e0
Wenshan Wu
6528981f94bb032403d407e1
Yaobo Liang
admin_assigned
2023-10-13T08:42:06.725Z
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Yaobo Liang
user
Eureka6174
6528981f94bb032403d407e2
Shaoguang Mao
admin_assigned
2023-10-13T08:42:15.245Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/o37G6VkKSyP4N9xktFErV.png
Shaoguang Mao
user
dawnmsg
6528981f94bb032403d407e3
Chenfei Wu
admin_assigned
2023-10-13T08:42:23.076Z
63180f0f49af4d408c35f07f
https://cdn-avatars.huggingface.co/v1/production/uploads/1678864054390-63180f0f49af4d408c35f07f.jpeg
Chenfei Wu
user
Chenfei
6528981f94bb032403d407e4
Maosong Cao
admin_assigned
2023-10-13T08:42:34.451Z
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/avatars/c01a98d57733cfc29b549b974d4e2da4.svg
maosongcao
user
bittersweet
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Yuzhe Cai
admin_assigned
2023-10-13T08:42:44.018Z
64c21afe5b329de1b1e103b2
/avatars/64c6981342092d8874b51505ce9d9109.svg
Yuzhe Cai
user
YZhe
6528981f94bb032403d407e6
Yiduo Guo
admin_assigned
2023-10-13T08:43:01.646Z
64e3282156b920ef00cf7d94
/avatars/8a76c27fe6c71eb98a28b4ebfb90336d.svg
Guo
user
YiDuo1999
6528981f94bb032403d407e7
Yan Xia
6528981f94bb032403d407e8
Furu Wei
admin_assigned
2023-10-13T08:43:45.928Z
6368c512fbfe97c16a40baba
/avatars/1c23bc7c0b6d9225699ce27647623d7a.svg
Furu Wei
user
thegenerality
6528981f94bb032403d407e9
Nan Duan
EIPE-text: Evaluation-Guided Iterative Plan Extraction for Long-Form
  Narrative Text Generation
Plan-and-Write is a common hierarchical approach in long-form narrative text
generation, which first creates a plan to guide the narrative writing.
Following this approach, several studies rely on simply prompting large
language models for planning, which often yields suboptimal results. In this
paper, we propose a new framework called Evaluation-guided Iterative Plan
Extraction for long-form narrative text generation (EIPE-text), which extracts
plans from the corpus of narratives and utilizes the extracted plans to
construct a better planner. EIPE-text has three stages: plan extraction,
learning, and inference. In the plan extraction stage, it iteratively extracts
and improves plans from the narrative corpus and constructs a plan corpus. We
propose a question answer (QA) based evaluation mechanism to automatically
evaluate the plans and generate detailed plan refinement instructions to guide
the iterative improvement. In the learning stage, we build a better planner by
fine-tuning with the plan corpus or in-context learning with examples in the
plan corpus. Finally, we leverage a hierarchical approach to generate long-form
narratives. We evaluate the effectiveness of EIPE-text in the domains of novels
and storytelling. Both GPT-4-based evaluations and human evaluations
demonstrate that our method can generate more coherent and relevant long-form
narratives. Our code will be released in the future.
The proposed EIPE-text framework iteratively extracts and refines narrative plans from a corpus to improve long-form text generation, resulting in more coherent and relevant narratives.
plan extraction plan refinement plan corpus question answer fine-tuning in-context learning long-form narrative text generation