Zero-Shot Learning for Obsolescence Risk Forecasting

LAAS-CNRS, 7 Av du Colonel Roche, 31400, Toulouse, France (e-mail: claude.baron@insa-toulouse.fr)Abstract: 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. INTRODUCTION Obsolescence is a significant challenge for industries relying on electronic components and complex systems. It occurs when products become outdated or unavailable due to technological advancements, market changes, or new regulations (International Electrotechnical Commission, 2019), leading to increased maintenance costs, disruptions in the security and availability of systems (Zolghadri et al., 2023), and operational inefficiencies (Mellal, 2020).