LogiCode: an LLM-Driven Framework for Logical Anomaly Detection

TL;DR

LogiCode introduces an LLM-based framework that autonomously detects logical anomalies in industrial data by generating Python code, improving accuracy and interpretability over traditional methods.

Contribution

The paper presents LogiCode, a novel LLM-driven framework with a custom dataset and benchmark for logical anomaly detection in industrial settings.

Findings

Enhanced detection accuracy compared to traditional methods

High success rate in code generation for anomaly identification

Improved interpretability with detailed explanations

Abstract

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.