With carefully layered prompts and one or three reference samples, GPT-4.1 detects anomalies in cable images and crimp-force features at F1 levels that PatchCore and Isolation Forest reach only after training on dozens to hundreds of samples.
Refining Time Series Anomaly Detectors using Large Language Models
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abstract
Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing. Despite the development of numerous automatic methods for detecting anomalies, human oversight remains necessary to review and act upon detected anomalies, as well as verify their accuracy. We study the use of multimodal large language models (LLMs) to partially automate this process. We find that LLMs can effectively identify false alarms by integrating visual inspection of time series plots with text descriptions of the data-generating process. By leveraging the capabilities of LLMs, we aim to reduce the reliance on human effort required to maintain a TSAD system
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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments
With carefully layered prompts and one or three reference samples, GPT-4.1 detects anomalies in cable images and crimp-force features at F1 levels that PatchCore and Isolation Forest reach only after training on dozens to hundreds of samples.