Frankentext: Stitching random text fragments into long-form narratives

ICLR 2026 Conference Submission22281 Authors

Keywords: LLMs, narrative generation, machine-generated text detection, human-AI collaboration, long-form generation, creative writing

TL;DR: We present a new type of narratives produced by LLMs under the extreme constraint that most tokens are copied from human writing. We evaluate these narratives and explore their implications for authorship, AI text detection and collaborative writing.

Abstract: We introduce Frankentexts, a long-form narrative generation paradigm that treats a large language model (LLM) as a composer of existing texts rather than as an author. Given a writing prompt and thousands of randomly sampled human-written snippets, the model is asked to produce a narrative under the extreme constraint that most tokens (e.g., 90%) must be copied verbatim from the provided paragraphs. This task is effectively intractable for humans: selecting and ordering snippets yields a combinatorial search space that an LLM implicitly explores, before minimally editing and stitching together selected fragments into a coherent long-form story. Despite the extreme challenge, we observe through extensive automatic and human evaluation that Frankentexts significantly improve over vanilla LLM generations in terms of writing quality, diversity, and originality, while remaining coherent and relevant to the prompt. Furthermore, Frankentexts pose a fundamental challenge to detectors of AI-generated text: 72% of Frankentexts produced by our best Gemini 2.5 Pro configuration are misclassified as human-written by Pangram, a state-of-the-art detector. Human annotators praise Frankentexts for their inventive premises, vivid descriptions, and dry humor; on the other hand, they identify issues with abrupt tonal shifts and uneven grammar across segments, particularly in longer pieces. The emergence of high-quality Frankentexts raises serious questions about authorship and copyright: when humans provide the raw materials and LLMs orchestrate them into new narratives, who truly owns the result?

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 22281