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New framework analyzes user prompt edits for story generation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework to analyze how users iteratively edit prompts when generating stories with large language models. This framework, based on a dataset of user-chatbot conversations, categorizes edits into four types (adding, removing, changing, extending) across fourteen targets like plot and character. The study aims to better understand user behavior in navigating narrative possibilities and proposes using these insights for improved story generation benchmarking. AI

IMPACT Provides a new methodology for evaluating LLM story generation capabilities and understanding user creative processes.

RANK_REASON The cluster contains a research paper detailing a new framework for analyzing user interactions with LLMs for story generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework analyzes user prompt edits for story generation

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The cluster contains a research paper detailing a new framework for analyzing user interactions with LLMs for story generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Advait Deshmukh, Nora Benedict, Melanie Walsh, Maria Antoniak ·

    The Garden of Forking Prompts: How Users Explore Narrative Space in Story Generation

    arXiv:2609.14677v1 Announce Type: new Abstract: Large language models (LLMs) have changed the way people engage with stories. Drawing on public chatbot logs, we can see that when users generate stories, they iteratively edit their prompts to explore narrative possibilities, adjus…

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