USC-FORTIS/AD-LLM
[ACL Findings 2025] A benchmark for anomaly detection using large language models. It supports zero-shot detection, data augmentation, and model selection, with scripts and data for GPT-4 and Llama experiments.
This project helps machine learning researchers and practitioners evaluate how Large Language Models (LLMs) perform in anomaly detection tasks involving natural language. It takes various LLMs (like GPT-4 or Llama) and natural language datasets, providing benchmark results for zero-shot detection, data augmentation, and model selection. Researchers working on advancing NLP-driven anomaly detection would find this useful.
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Use this if you are a machine learning researcher or engineer looking to benchmark LLMs for natural language anomaly detection, explore their ability to generate synthetic data, or recommend suitable unsupervised models.
Not ideal if you are an end-user seeking a ready-to-use anomaly detection application for your specific business data, as this is a research benchmark.
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Language
Python
License
MIT
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Last pushed
Oct 09, 2025
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