Summary of Llm-based Knowledge Pruning For Time Series Data Analytics on Edge-computing Devices, by Ruibing Jin et al.
LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices
by Ruibing Jin, Qing Xu, Min Wu, Yuecong Xu, Dan Li, Xiaoli Li, Zhenghua Chen
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed Knowledge Pruning (KP) paradigm tackles the issue of neural networks overfitting when trained on time series data by pruning redundant knowledge learned from large language models (LLMs). This approach distills only the pertinent knowledge into a target model, reducing its size and computational costs. Unlike existing LLM-based methods, KP does not require loading the entire LLM during training and testing, making it suitable for edge-computing devices. The authors demonstrate the effectiveness of KP by achieving state-of-the-art results in regression (19.7% on average) and classification (up to 13.7%) tasks on eight diverse environments or benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KP is a new way to train neural networks on time series data. It works by taking the knowledge learned from large language models and only keeping the parts that are actually useful for a specific task. This makes the model smaller and uses less computer power, which is important for devices like smart home appliances or sensors in factories. The researchers tested KP on two types of tasks and found it worked really well, even better than other methods that use more powerful computers. |
Keywords
» Artificial intelligence » Classification » Overfitting » Pruning » Regression » Time series
