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.
Abstract (translated)
训练数据的质量对于交通运输、医疗保健和机器人技术等领域的机器学习应用性能至关重要。然而，准确的图像标注通常依赖于耗时且需要专业知识的方法，并且反馈有限。这项研究引入了一种基于草图注释的方法，该方法得到了大型语言模型（LLM）的支持，以降低技术壁垒并提高可访问性。通过使用合成数据集，我们考察了草图识别特征与LLM反馈指标之间的关系，旨在提升LLM辅助标注的可靠性和解释性。此外，我们还探讨了提示策略和草图变化对反馈质量的影响。 我们的主要贡献是一款基于草图的虚拟助手，它简化了非专业人士的注释过程，并在可扩展性、易用性和可解释性方面推进了LLM驱动的标记工具的发展。
URL
https://arxiv.org/abs/2505.19419
