LogiCode: An LLM-Driven Framework for Logical Anomaly Detection

Yiheng Zhang

doi:10.1109/TASE.2024.3468464

LogiCode: An LLM-Driven Framework for Logical Anomaly Detection

2024-01-01

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AI

DOI：10.1109/TASE.2024.3468464

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This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond the 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. Our code are available at https://github.com/22strongestme/LOCO-Annotations.

Keyword:

Anomaly detection

Codes

Cognition

Visualization

Semantics

Logic

Image reconstruction

Automation

Accuracy

Benchmark testing

Logical anomaly detection

large language models

industrial anomaly detection

dataset annotation

LogiCode: An LLM-Driven Framework for Logical Anomaly Detection

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