LAMPER: LanguAge Model and Prompt EngineeRing for zero-shot time series classification

Autor:	Du, Zhicheng, Xie, Zhaotian, Tong, Yan, Qin, Peiwu
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted as tiny paper in ICLR 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.15875 Zobrazit plný text záznamu View this record from Arxiv