标题： 零样本学习用于过时风险预测 显示英文标题

标题： Zero-Shot Learning for Obsolescence Risk Forecasting

摘要： 组件过时在依赖电子元件的工业中带来了重大挑战，导致成本增加以及系统安全性和可用性的中断。 准确的过时风险预测至关重要，但受限于缺乏可靠的数据。 本文提出了一种新的方法，利用零样本学习（ZSL）和大型语言模型（LLMs）来解决数据限制问题，通过利用表格数据集中的领域专业知识进行预测。 应用于两个真实数据集，该方法展示了有效的风险预测能力。 对四种LLMs的比较评估突显了为特定预测任务选择合适模型的重要性。 显示英文摘要

摘要： Component obsolescence poses significant challenges in industries reliant on electronic components, causing increased costs and disruptions in the security and availability of systems. Accurate obsolescence risk prediction is essential but hindered by a lack of reliable data. This paper proposes a novel approach to forecasting obsolescence risk using zero-shot learning (ZSL) with large language models (LLMs) to address data limitations by leveraging domain-specific knowledge from tabular datasets. Applied to two real-world datasets, the method demonstrates effective risk prediction. A comparative evaluation of four LLMs underscores the importance of selecting the right model for specific forecasting tasks.