LogiCode: an LLM-Driven Framework for Logical Anomaly Detection
This addresses the problem of detecting logical anomalies beyond structural inconsistencies for industrial applications, representing a notable shift towards more intelligent approaches.
The paper tackles logical anomaly detection in industrial settings by introducing LogiCode, an LLM-driven framework that generates Python code to identify anomalies like incorrect component quantities, resulting in improved accuracy and interpretability with metrics such as binary classification accuracy and code generation success rate.
This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond traditional focus on structural inconsistencies. By harnessing LLMs for logical reasoning, LogiCode autonomously generates Python codes to pinpoint anomalies such as incorrect component quantities or missing elements, marking a significant leap forward in anomaly detection technologies. A custom dataset "LOCO-Annotations" and a benchmark "LogiBench" are introduced to evaluate the LogiCode's performance across various metrics including binary classification accuracy, code generation success rate, and precision in reasoning. Findings demonstrate LogiCode's enhanced interpretability, significantly improving the accuracy of logical anomaly detection and offering detailed explanations for identified anomalies. This represents a notable shift towards more intelligent, LLM-driven approaches in industrial anomaly detection, promising substantial impacts on industry-specific applications.