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Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers

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arxiv 2309.12570 v3 pith:TCPJWAZE submitted 2023-09-22 cs.HC cs.AIcs.CLcs.CY

classification cs.HCcs.AIcs.CLcs.CY
keywords llmswritingcognitiveinteractionswritersacrossactivitiesanalyzing
verification ladder T0 review T1 audit T2 compute T3 formal
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The development of large language models (LLMs) capable of following instructions and engaging in conversational interactions sparked increased interest in their utilization across various support tools. We investigate the utility of modern LLMs in assisting professional writers via an empirical user study (n=30). The design of our collaborative writing interface is grounded in the cognitive process model of writing that views writing as a goal-oriented thinking process encompassing non-linear cognitive activities: planning, translating, and reviewing. Participants are asked to submit a post-completion survey to provide feedback on the potential and pitfalls of LLMs as writing collaborators. Upon analyzing the writer-LLM interactions, we find that while writers seek LLM's help across all three types of cognitive activities, they find LLMs more helpful in translation and reviewing. Our findings from analyzing both the interactions and the survey responses highlight future research directions in creative writing assistance using LLMs.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 10 citations worldwide. Full citation record

  1. Intelligent Manufacturing Support: Specialized LLMs for Composite Material Processing and Equipment Operation

    stat.AP 2025-09 conditional novelty 4.0 of 10

    A RAG-augmented GPT-4 assistant with curated composites and machine-manual documents produces expert-preferred but not benchmark-clearly-better answers than plain GPT-4o.

  2. A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations

    cs.CL 2025-07 conditional novelty 4.0 of 10

    PATTR adds a target-length penalty to the Type-Token Ratio, producing a lexical diversity score with tunable, reduced short-text bias for LLM synthetic data.

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