@inproceedings{spangher-etal-2025-creative,
title = "Creative Planning with Language Models: Practice, Evaluation and Applications",
author = "Spangher, Alexander and
Huang, Tenghao and
Laban, Philippe and
Peng, Nanyun",
editor = "Lomeli, Maria and
Swayamdipta, Swabha and
Zhang, Rui",
booktitle = "Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-tutorial.1/",
doi = "10.18653/v1/2025.naacl-tutorial.1",
pages = "1--9",
ISBN = "979-8-89176-193-3",
abstract = "The use of large language models (LLMs) in human-centered creative tasks {---} such as journalism, scientific writing, and storytelling {---} has showcased their potential for content generation but highlighted a critical gap: planning. Planning, used here to describe the ``actions'' humans perform before (and during) the writing process, is a fundamental process in many creative domains. This tutorial explores how planning has been learned and deployed in creative workflows, unifying three scenarios: Full Data Regimens (when observational data for actions and the resulting text exist), Partial (when text exists but actions can be inferred) and Low (when neither exist). The tutorial discusses forward and backward learning approaches for planning in LLMs, evaluation metrics tailored to latent plans, and practical applications in computational journalism, web agents, and other creative domains. By bridging theoretical concepts and practical demonstrations, this tutorial aims to inspire new research directions in leveraging LLMs for creative and goal-oriented planning tasks."
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<abstract>The use of large language models (LLMs) in human-centered creative tasks — such as journalism, scientific writing, and storytelling — has showcased their potential for content generation but highlighted a critical gap: planning. Planning, used here to describe the “actions” humans perform before (and during) the writing process, is a fundamental process in many creative domains. This tutorial explores how planning has been learned and deployed in creative workflows, unifying three scenarios: Full Data Regimens (when observational data for actions and the resulting text exist), Partial (when text exists but actions can be inferred) and Low (when neither exist). The tutorial discusses forward and backward learning approaches for planning in LLMs, evaluation metrics tailored to latent plans, and practical applications in computational journalism, web agents, and other creative domains. By bridging theoretical concepts and practical demonstrations, this tutorial aims to inspire new research directions in leveraging LLMs for creative and goal-oriented planning tasks.</abstract>
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%0 Conference Proceedings
%T Creative Planning with Language Models: Practice, Evaluation and Applications
%A Spangher, Alexander
%A Huang, Tenghao
%A Laban, Philippe
%A Peng, Nanyun
%Y Lomeli, Maria
%Y Swayamdipta, Swabha
%Y Zhang, Rui
%S Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-193-3
%F spangher-etal-2025-creative
%X The use of large language models (LLMs) in human-centered creative tasks — such as journalism, scientific writing, and storytelling — has showcased their potential for content generation but highlighted a critical gap: planning. Planning, used here to describe the “actions” humans perform before (and during) the writing process, is a fundamental process in many creative domains. This tutorial explores how planning has been learned and deployed in creative workflows, unifying three scenarios: Full Data Regimens (when observational data for actions and the resulting text exist), Partial (when text exists but actions can be inferred) and Low (when neither exist). The tutorial discusses forward and backward learning approaches for planning in LLMs, evaluation metrics tailored to latent plans, and practical applications in computational journalism, web agents, and other creative domains. By bridging theoretical concepts and practical demonstrations, this tutorial aims to inspire new research directions in leveraging LLMs for creative and goal-oriented planning tasks.
%R 10.18653/v1/2025.naacl-tutorial.1
%U https://aclanthology.org/2025.naacl-tutorial.1/
%U https://doi.org/10.18653/v1/2025.naacl-tutorial.1
%P 1-9
Markdown (Informal)
[Creative Planning with Language Models: Practice, Evaluation and Applications](https://aclanthology.org/2025.naacl-tutorial.1/) (Spangher et al., NAACL 2025)
ACL
- Alexander Spangher, Tenghao Huang, Philippe Laban, and Nanyun Peng. 2025. Creative Planning with Language Models: Practice, Evaluation and Applications. In Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts), pages 1–9, Albuquerque, New Mexico. Association for Computational Linguistics.