Zero-Shot Learning for Obsolescence Risk Forecasting

TL;DR

This paper introduces a zero-shot learning approach using large language models to predict component obsolescence risk in industries, effectively addressing data scarcity issues and improving forecasting accuracy.

Contribution

It presents a novel application of zero-shot learning with LLMs for obsolescence risk forecasting, leveraging domain knowledge from tabular data to overcome data limitations.

Findings

Effective risk prediction demonstrated on real-world datasets

Model selection impacts forecasting accuracy significantly

Zero-shot learning reduces reliance on extensive historical data

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.