Creative Planning with Language Models: Practice, Evaluation and Applications (2025)

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.

## introduction llms have demonstrated impressive generative capacities across a range of tasks. however, many human creative tasks (e.g. in journalism, scientific writing, video script writing and creative story generation) involve extensive planning. for example, a human journalist typically follows a multi-step process before they are even ready to write a news article (e.g. "find story idea" → "develop angle" → "find informational sources" → "get quotes" → "confirm facts") @xcite ). an emerging body of work has pointed to key short-comings of llms and opportunities for progress in domains where planning is required, actions need to be taken and objectives are poorly defined. many emerging tasks in nlp can be framed as "planning" tasks: either those that are explicitly using llms as planning-agents (e.g. @xcite ) or those that attempt to infer or learn from the plans guiding human text generation @xcite . in this tutorial, we aim to bring tasks in this umbrella into dialogue. can the ability to plan make llms become more useful, more human-like and more attuned to the needs of diverse creative professionals? we aim to consolidate an emerging direction of work that lies in the intersection of: creative generation, agentic planning, and human-centered nlp. ## three aspects of creativity the main structure of our tutorial breaks down creative planning into three main stages: problem-finding, path-finding and evaluation. each section is grounded in a large history cognitive science literature. we cover each stage in turn. ## data regimes: full, partial and low in order to conceptualize methods required to study creative planning, we divide creative tasks into three categories based on the availability of data: full visibility, partial visibility and low visibility. to frame these categories, we use vocabulary from the field of reinforcement learning: actions refers to planning steps or inferences the model can take. state-space refers broadly to textual states (e.g. utterances, documents or retrievals) that are caused or influenced by actions. low data regimens: settings in which little-tono data is available about the planning process, including either the end-states or any of the actions or states in between. examples of tasks in this domain, including: osworld @xcite , webarena @xcite and other web-agent tasks @xcite @xcite @xcite , where the language model is tasked with navigating webpages without any examples of the output. partial data regiments: settings where endstate information, but no actions, are available to the planning process. tasks in this planning domain encompass fields like: computational journalism @xcite , computational law @xcite , scientific writing @xcite and creative fictional writing @xcite . in these tasks, it is typically cheap to collect voluminous datasets of finished news articles, for instance, but it is typically too expensive to observe actions leading up to the finished articles. partial-to-full data regiments are characterized by situations in which pre-final text and/or action sequences are available for the models to train on. we briefly introduce various tasks and domains where datasets have emerged to support these plans plans, such as tool learning @xcite @xcite , edit prediction @xcite , math problem-solving @xcite and instruction-learning @xcite . in these settings, more of a supervised approach can be taken to learn plans. ## application domains of creative planning: demonstrations having established a better definition for "plans" and methods for inferring plans from observed text, we close by discussing applications in various domains. we will give live demonstration of creative tools and compare tools that do not formally plan (e.g. those that engineer sequences of prompts) with tools that do. computational journalism (cj) this field aims to build decision-support tools for journalists to help find stories and sources; verifying facts; and write articles @xcite . cj gives us a good example of a domain of tasks where (1) abundant medium-visibility data exists (2) professional standards across organizations dictate regular and formalized planning and (3) outcomes are socially beneficial. recent tasks in cj include: "help a journalist find informational sources to support the story" @xcite @xcite , "find newsworthy stories to cover" @xcite @xcite , "plan longer-term article structures" @xcite @xcite . we will showcase tools without formalized planning, such as anglekindling, a tool for angle selection in journalistic writing @xcite . we then demonstrate tools that learn and utilize latent plans to enhance output quality, such as newssources @xcite and spinach @xcite . proactive task-oriented agents this field aims to build agents to proactively identify and clarify missing or ambiguous information essential to methodical, domain-specific tasks @xcite @xcite . by systematically examining the effects of partially available information, these methods train models to optimally balance the cost of queries against the improved accuracy and completeness of their outputs. this proactive reasoning capability significantly enhances the practical utility, creativity and reliability of task-oriented agents, particularly in high-stakes, informationsensitive environments, and represents a promising direction for human-ai collaborative systems. creative writing and editing planning plays a crucial role in creative language generation, especially in long-form text generation. content planning, such as sketching out plot points @xcite @xcite , has been shown to improve the quality of generated stories and for generating creative outputs like poetry, where form constraints must be adhered to @xcite , or metaphor or figurative language @xcite ) must be used. incorporating knowledge into the planning process can significantly enhance the ability of llms to produce more nuanced, creative outputs @xcite . ## broader relevance: connection to existing fields we situate creative planning in a broader field of artificial intelligence and natural language processing, with explicit intersections in: we consider the following skills useful for researchers considering making advances in creative planning: ## suggested reading list summary this tutorial will include our own work, notably in the fields of computational journalism, creativity, latent variable modeling and agent modeling @xcite @xcite @xcite and work by other researchers in nlp and machine learning communities, including but not limited to: @xcite @xcite @xcite @xcite .