Summary of Decoding Time Series with Llms: a Multi-agent Framework For Cross-domain Annotation, by Minhua Lin et al.
Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation
by Minhua Lin, Zhengzhang Chen, Yanchi Liu, Xujiang Zhao, Zongyu Wu, Junxiang Wang, Xiang Zhang, Suhang Wang, Haifeng Chen
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a multi-agent system called TESSA to automatically generate high-quality annotations for time series data in various domains, including manufacturing, finance, and healthcare. The system consists of two agents: a general annotation agent that captures common patterns across multiple source domains, and a domain-specific annotation agent that learns domain-specific terminology from limited target-domain annotations. The proposed method is evaluated on multiple synthetic and real-world datasets, demonstrating its effectiveness in generating high-quality annotations that outperform existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TESSA helps with time series data by making it easier to understand what’s happening in different areas like factories, banks, and hospitals. Right now, getting good labels for this kind of data is hard, especially when it’s really important. The researchers developed a system with two parts: one that looks at patterns across lots of places, and another that learns special words used in specific fields. They tested their idea on several sets of real and fake data and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Time series
