In this paper, we propose a feature pioneering method using Large Language Models (LLMs). In the proposed method, we use Chat-GPT 1 to find new sensor locations and new features. Then we evaluate the machine learning model which uses the found features using Opportunity Dataset [ 4 , 9]. In current machine learning, humans make features, for this engineers visit real sites and have discussions with experts and veteran workers. However, this method has the problem that the quality of the features depends on the engineer. In order to solve this problem, we propose a way to make new features using LLMs. As a result, we obtain almost the same level of accuracy as the proposed model which used fewer sensors and the model uses all sensors in the dataset. This indicates that the proposed method is able to extract important features efficiently.

翻译：本文提出了一种利用大型语言模型（LLMs）进行特征先驱探索的方法。在该方法中，我们使用Chat-GPT 1来发现新的传感器位置及新特征，随后基于Opportunity数据集[4, 9]评估采用所发现特征的机器学习模型性能。当前机器学习领域中，特征通常由人工构建，工程师需实地考察并与领域专家及资深工作者进行讨论。然而，该方法存在特征质量依赖工程师经验的问题。为解决此问题，我们提出利用LLMs生成新特征的方案。实验结果表明，相较于使用数据集中全部传感器的模型，所提模型在采用更少传感器的情况下达到了几乎相同的精度水平，验证了该方法能够高效提取关键特征。