Work

AD-LLM: Benchmarking Large Language Models for Anomaly Detection

Year: 2025

Type: conference-paper

Abstract: Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao. Findings of the Association for Computational Linguistics: ACL 2025. 2025.

DOI: 10.18653/v1/2025.findings-acl.79

Authors: Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao

Institutions: University of Southern California, Northwestern University, Arizona State University, Rice University, Adobe Systems (United States)

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: National Science Foundation, National Centre for Supercomputing Applications, Office of Advanced Cyberinfrastructure

Awards: G1353381076, G1453181110, G1503004319, NSF POSE: Phase II: OpenAD: An Integrated Open-Source Ecosystem for Anomaly Detection, Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, G298330452, G3562386289, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), G4010157413, G4403015908, ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI), Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G5367127336, G5464919783, ATD: Multifaceted Large Trajectory Model for Individual and Group Anomalous Behavior Detection from Spatiotemporal Data, G8108599699, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI

OpenAlex ID: W4412888853

FWCI: 10.82

Cites:

Cited by: 11

Related to: 10