REVIEW 2 cited by
Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 2 Pith papers
-
Intelligent Manufacturing Support: Specialized LLMs for Composite Material Processing and Equipment Operation
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.
-
A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations
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.
Discussion (0). Sign in to comment.