# It's Not Just Labeling -- A Research on LLM Generated Feedback Interpretability and Image Labeling Sketch Features

arXiv: https://arxiv.org/abs/2505.19419 · HTML: https://mlsanity.com/paper/2505.19419/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 5 of 5)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [A survey of image labelling for computer vision applications](https://mlsanity.com/paper/2104.08885/) (2021)
- [Can Active Label Correction Improve LLM-based Modular AI Systems?](https://mlsanity.com/paper/2401.05467/) (2024)
- [Panoptic Image Annotation with a Collaborative Assistant](https://mlsanity.com/paper/1906.06798/) (2019)

## Built on by (most cited first, 1 of 1)

- [From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review](https://mlsanity.com/paper/2605.12303/) (2026)

## Abstract (arXiv)

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.