aceStylometric comparisons of human versus AI-generated creative writing
This study employs stylometry to investigate whether the creative writing styles of humans and large language models (LLMs) such as GPT-3.5, GPT-4, and Llama 70b can be distinguished through quantitative analysis. A balanced dataset of short stories composed in response to predefined narrative prompts forms the basis of the analysis. Burrows’ Delta, a widely used metric in computational literary studies, is applied to measure stylistic similarity and difference across texts. By focusing on the distribution of th…
Quantitative stylometry using Burrows' Delta can reliably separate LLM-generated short stories from human-authored stories, providing a measurable tool for authenticity and authorship checks.
LLM-generated creative writing shows higher stylistic uniformity and tight clustering by model, lacking the broader heterogeneity and individual diversity seen in human-authored stories.
Findings are based on a balanced dataset of short stories composed in response to predefined narrative prompts, and occasional overlaps between GPT-3.5 and human texts were observed, limiting generalizability to other genres or open-ended writing.
Evidence
- Peer-reviewedHumanities and Social Sciences Communications2025-11-11
How should this claim be treated?
Truvace Impact Record TRV-2026-0883, v1: “Stylometric comparisons of human versus AI-generated creative writing.” Truvace, 2026-08-25. /record/TRV-2026-0883 (accessed at citation time). sha256 08590170145a3b7f…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0883 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.