LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866913279569297408 |
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| author | Du, Zhicheng Xie, Zhaotian Tong, Yan Qin, Peiwu |
| author_facet | Du, Zhicheng Xie, Zhaotian Tong, Yan Qin, Peiwu |
| contents | This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework, designed to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time series (TS) classification. We deploy LAMPER in experimental assessments using 128 univariate TS datasets sourced from the UCR archive. Our findings indicate that the feature representation capacity of LAMPER is influenced by the maximum input token threshold imposed by PLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15875 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification Du, Zhicheng Xie, Zhaotian Tong, Yan Qin, Peiwu Artificial Intelligence Computation and Language This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework, designed to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time series (TS) classification. We deploy LAMPER in experimental assessments using 128 univariate TS datasets sourced from the UCR archive. Our findings indicate that the feature representation capacity of LAMPER is influenced by the maximum input token threshold imposed by PLMs. |
| title | LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2403.15875 |