TingIS system uses LLMs to find critical incidents in real-time

Researchers have developed TingIS, a novel system designed to identify critical technical issues in real-time from noisy customer feedback. The system employs a multi-stage engine that combines Large Language Models with efficient indexing to merge and extract actionable incidents from user descriptions. TingIS also incorporates a cascaded routing mechanism for business attribution and a noise reduction pipeline, achieving a 95% discovery rate for high-priority incidents with a P90 alert latency of 3.5 minutes in a production environment. AI

IMPACT This system could significantly reduce downtime for cloud services by enabling faster discovery and mitigation of critical technical issues.

RANK_REASON The cluster contains an academic paper detailing a new system and its performance benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Large Language Models
• TingIS
• Ziyin Zhang

• paper
• product
• infra

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