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cs.CL2024
Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection
Jun Liu, Chaoyun Zhang, Jiaxu Qian +6
Time series anomaly detection (TSAD) plays a crucial role in various industries by identifying atypical patterns that deviate from standard trends, thereby maintaining system integ…
cs.CL2024
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback-based Self-Correction
Xiang Huang, Sitao Cheng, Shanshan Huang +4
Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success. However, we find existing methods fall short in terms of reliability and efficiency whe…
cs.CL2024
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments
Sitao Cheng, Ziyuan Zhuang, Yong Xu +9
Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graph and table. Such tasks typically require multi-hop reasoning, i.e.…