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Is Generative AI Hiding in Your Phone? Fine-tuning SLMs to Generate JSON-Adherent Output

Angenius, Simon LU (2026) EDAN70 20261
Department of Computer Science
Abstract
How long will it be before our modern society is wrapped in generative AI without our discernible knowledge? What happens when the line between real and synthetic is practically non-existent? This study explores how a Qwen3-0.6B model, designed for mobile and edge deployment, can be fine-tuned to generate JSON-schematic data for workout and fitness. It shows that even smaller models are well-enough to design parsable output, ready to be used by an application - thus hiding its AI trademarks. In the future, studies on even smaller models may be appropriate, as well as discussions regarding its ethics.
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author
Angenius, Simon LU
supervisor
organization
course
EDAN70 20261
year
type
L3 - Miscellaneous, Projetcs etc.
subject
language
English
id
9249673
date added to LUP
2026-09-01 13:57:52
date last changed
2026-09-01 13:57:52
@misc{9249673,
  abstract     = {{How long will it be before our modern society is wrapped in generative AI without our discernible knowledge? What happens when the line between real and synthetic is practically non-existent? This study explores how a Qwen3-0.6B model, designed for mobile and edge deployment, can be fine-tuned to generate JSON-schematic data for workout and fitness. It shows that even smaller models are well-enough to design parsable output, ready to be used by an application - thus hiding its AI trademarks. In the future, studies on even smaller models may be appropriate, as well as discussions regarding its ethics.}},
  author       = {{Angenius, Simon}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Is Generative AI Hiding in Your Phone? Fine-tuning SLMs to Generate JSON-Adherent Output}},
  year         = {{2026}},
}