All That Glitters is Not Novel: Plagiarism in AI Generated Research

Tarun Gupta, Danish Pruthi


Abstract
Automating scientific research is considered the final frontier of science. Recently, several papers claim autonomous research agents can generate novel research ideas. Amidst the prevailing optimism, we document a critical concern: a considerable fraction of such research documents are smartly plagiarized. Unlike past efforts where experts evaluate the novelty and feasibility of research ideas, we request 13 experts to operate under a different situational logic: to identify similarities between LLM-generated research documents and existing work. Concerningly, the experts identify 24% of the 50 evaluated research documents to be either paraphrased (with one-to-one methodological mapping), or significantly borrowed from existing work. These reported instances are cross-verified by authors of the source papers. Experts find an additional 32% ideas to partially overlap with prior work, and a small fraction to be completely original. Problematically, these LLM-generated research documents do not acknowledge original sources, and bypass inbuilt plagiarism detectors. Lastly, through controlled experiments we show that automated plagiarism detectors are inadequate at catching plagiarized ideas from such systems. We recommend a careful assessment of LLM-generated research, and discuss the implications of our findings on academic publishing.
Anthology ID:
2025.acl-long.1249
Volume:
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
25721–25738
Language:
URL:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1249/
DOI:
Bibkey:
Cite (ACL):
Tarun Gupta and Danish Pruthi. 2025. All That Glitters is Not Novel: Plagiarism in AI Generated Research. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 25721–25738, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
All That Glitters is Not Novel: Plagiarism in AI Generated Research (Gupta & Pruthi, ACL 2025)
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PDF:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1249.pdf