Misinformational AI-Generated Academic Papers

Aaron Sandoval, Akash Kundu, Layla Adam

This study explores the potential for generative AI to produce convincing fake research papers, highlighting the growing threat of AI-generated misinformation. We demonstrate a semi-automated pipeline using large language models (LLMs) and image generation tools to create academic-style papers from simple text prompts.

Reviewer's Comments

Reviewer's Comments

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Lucas Hansen

Very cool idea! This is solid progress towards auto-generation of research papers. I particularly liked (1) including figures & (2) starting with a many similar papers as a baseline.I think it would’ve been nice to include some research papers that you consider problematic as examples.

Épiphanie Gédéon

Good idea!I feel like it would have been better to generate a paper proving a point of view generated by the user, as the frontend only allows downloading the generated paper, which doesn’t prove automation.

Adam Binksmith

Nice work! Showing rendered LaTeX is a nice direct way to demonstrate LLMs’ ability to generate it. For next steps, I’d be interested to see a more interactive version of this demo, and more support for the user in understanding where the generated paper is or isn’t plausible.

Cite this work

@misc {

title={

Misinformational AI-Generated Academic Papers

},

author={

Aaron Sandoval, Akash Kundu, Layla Adam

},

date={

8/26/24

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

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We estimate parameters from real-world data (e.g., adversarial success rates, detection latency, patching delays) and simulate attack propagation scenarios across 8 sectors (enterprise, retail, trading, development, customer service, academia, medical, and critical infrastructure AI tools).

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.