It's Not Just Labeling -- A Research on LLM Generated Feedback Interpretability and Image Labeling Sketch Features
Journal:
arXiv
Published Date:
May 26, 2025
Abstract
The quality of training data is critical to the performance of machine
learning applications in domains like transportation, healthcare, and robotics.
Accurate image labeling, however, often relies on time-consuming, expert-driven
methods with limited feedback. This research introduces a sketch-based
annotation approach supported by large language models (LLMs) to reduce
technical barriers and enhance accessibility. Using a synthetic dataset, we
examine how sketch recognition features relate to LLM feedback metrics, aiming
to improve the reliability and interpretability of LLM-assisted labeling. We
also explore how prompting strategies and sketch variations influence feedback
quality. Our main contribution is a sketch-based virtual assistant that
simplifies annotation for non-experts and advances LLM-driven labeling tools in
terms of scalability, accessibility, and explainability.
