Summary of Lamper: Language Model and Prompt Engineering For Zero-shot Time Series Classification, by Zhicheng Du et al.
LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
by Zhicheng Du, Zhaotian Xie, Yan Tong, Peiwu Qin
First submitted to arxiv on: 23 Mar 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 study develops the LanguAge Model with Prompt EngineeRing (LAMPER) framework, which systematically evaluates the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time series (TS) classification. The researchers use LAMPER to assess 128 univariate TS datasets from the UCR archive, finding that the feature representation capacity is influenced by the maximum input token threshold imposed by PLMs. This study highlights the importance of considering these thresholds when evaluating PLM adaptability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how language models can be used for time series classification without needing to train them specifically for this task. The researchers create a special framework called LAMPER that tests how well language models work with different prompts and datasets. They use lots of different time series datasets to see if the language models are good at classifying them, and they find out that it depends on how much information each model can process. |
Keywords
» Artificial intelligence » Classification » Language model » Prompt » Time series » Token » Zero shot
