用大模型零样本预测电子元器件淘汰风险，解决数据不足难题
Zero-Shot Learning for Obsolescence Risk Forecasting
- 基于大语言模型的零样本学习，利用表格数据中的领域知识补足缺失信息
- 在两个真实数据集上实现有效风险预测，验证方法可行性
- 实证表明模型选择对预测效果影响显著，需针对任务选型
电子元器件淘汰给依赖电子元件的行业带来重大挑战，导致成本上升及系统安全与可用性中断。准确预测淘汰风险至关重要，但受限于可靠数据的缺乏。本文提出一种基于大语言模型的零样本学习方法，通过利用表格数据中的领域知识来克服数据限制，实现淘汰风险预测。该方法在两个真实世界数据集上得到应用，表现出良好的预测性能。对四种大语言模型的对比评估表明，模型选择对特定预测任务具有重要影响。
原文摘要 · Abstract (English)
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.
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