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Frankentext: Stitching random text fragments into long-form narratives

Paper · 2026 · Chau Minh Pham, Jenna Russell, Dzung Pham et al.

Summary

The paper asks whether a black-box LLM user can cheaply produce long stories that read well and still slip past strong AI-text detectors. The authors propose Frankentexts, where the LLM acts as an assembler of human prose rather than its writer. Given a writing prompt and thousands of random paragraphs sampled from Books3, a reasoning model drafts a story in which a target share of tokens (e.g., 90%) is copied verbatim. It adds only connective phrasing, then revises the draft in a few minimal-edit passes to fix coherence. Optional variants regenerate drafts flagged by Pangram or let agents query a snippet index via MCP. Automatic and human evaluations show that Frankentexts beat vanilla generations on quality, diversity and novelty while staying mostly coherent and on-prompt. Remaining weaknesses are sudden tonal shifts and uneven grammar between fragments. Most outputs are judged human-written by Pangram. Because every output carries token-level labels separating copied from generated spans, the pipeline also yields training data for mixed-authorship detection.

Key points

  • Source pool: Snippets are paragraphs from Books3 (197K books, over 160M snippets). The default prompt holds 1,500 snippets (about 103K tokens), and the strongest setup uses 5K snippets.
  • Detection result: With Gemini-2.5-Pro and 5K snippets, 72% of Frankentexts are misclassified as human-written by Pangram. Pangram's plagiarism detector flagged only 10–21% of outputs across Gemini, GPT-5 and Claude-4-Sonnet.
  • Quality results:
    • The LLM judge gives Frankentexts 4.21 versus 3.18 for vanilla output.
    • On NoveltyBench, Frankentexts reach 2.74 vs. 1.76 clusters and a utility of 9.27 vs. 6.41.
  • Model requirements: The task only works with reasoning models. Polishing runs at most three rounds, and the requested copy rate does not always match the actual attribution rate.
  • Proposed defenses and ethics: The authors suggest n-gram source matching and token-level attribution models as defenses. They stress copyright and plagiarism risks and restrict their use of Books3 to research.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of Maryland, College ParkCollege Park · United States132 works · 25,380 citations🎓University of Massachusetts AmherstAmherst Center · United States106 works · 66,692 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Chau Minh Pham, Jenna Russell, Dzung Pham and Mohit Iyyer. 2026. Frankentext: Stitching random text fragments into long-form narratives. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1457

@inproceedings{pham2026frankentext,
  title = {{Frankentext: Stitching random text fragments into long-form narratives}},
  author = {Chau Minh Pham, Jenna Russell, Dzung Pham and Mohit Iyyer},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1457},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1457}
}

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  96. Yiming Zhang, Harshita Diddee, Susan Holm, Hanchen Liu, Xinyue Liu, Vinay Samuel, Barry Wang, and Daphne Ippolito. 2025. Noveltybench: Evaluating creativity and diversity in language models. In Second Conference on Language Modeling. find
  97. Algorithm 1 Frankentexts generation pipeline Input: Human-written snippets S, writing guideline prompt P , copy rate threshold T Output: A Frankentext F “stitched” from S according to P 1: F ← Prompt LLM to draft a Frankentext using S and P // Ensure copy rate (optional) 2: copy_rate ← Calculate ROU… find

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