As AI text detectors are increasingly used to flag LLM-generated writing, a natural question arises: are there forms of high-quality generated narrative that can evade such detection?We introduce Frankentexts, a longform narrative generation paradigm that treats an 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 assembles a coherent narrative where most tokens (e.g., 90%) are copied verbatim from the source passages.Despite the extreme challenge of the task, we observe through extensive automatic and human evaluation that Frankentexts improve over vanilla LLM generations in key writing quality metrics such as diversity and novelty while remaining mostly coherent and relevant to the prompt.Furthermore, Frankentexts pose a fundamental challenge to current AI text detectors: 72% of Frankentexts produced by our best configuration (Gemini-2.5-Prowith 5K input snippets) 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; however, they still identify issues with abrupt tonal shifts and uneven grammar across segments.Overall, the emergence of highquality yet low-detectability Frankentexts challenges established authorship norms while raising concerns about the publishing economy.

Jenna Russell, Marzena Karpinska, Destiny Akinode, James Zhou, Katherine Thai, Bradley Emi, Max Spero, Mohit Iyyer. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

In this paper, we study how well humans can detect text generated by commercial LLMs (GPT-4o, Claude, o1). We hire annotators to read 300 non-fiction English articles, label them as either human-written or AI-generated, and provide paragraph-length explanations for their decisions. Our experiments show that annotators who frequently use LLMs for writing tasks excel at detecting AI-generated text, even without any specialized training or feedback. In fact, the majority vote among five such “expert” annotators misclassifies only 1 of 300 articles, significantly outperforming most commercial and open-source detectors we evaluated even in the presence of evasion tactics like paraphrasing and humanization. Qualitative analysis of the experts’ free-form explanations shows that while they rely heavily on specific lexical clues (‘AI vocabulary’), they also pick up on more complex phenomena within the text (e.g., formality, originality, clarity) that are challenging to assess for automatic detectors. We release our annotated dataset and code to spur future research into both human and automated detection of AI-generated text.