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.
@article{arxiv.2505.19419,
title = {It's Not Just Labeling -- A Research on LLM Generated Feedback Interpretability and Image Labeling Sketch Features},
author = {Baichuan Li and Larry Powell and Tracy Hammond},
journal= {arXiv preprint arXiv:2505.19419},
year = {2025}
}