  Light curves serve as a valuable source of information on stellar formation
and evolution. With the rapid advancement of machine learning techniques, it
can be effectively processed to extract astronomical patterns and information.
In this study, we present a comprehensive evaluation of deep-learning and large
language model (LLM) based models for the automatic classification of variable
star light curves, based on large datasets from the Kepler and K2 missions.
Special emphasis is placed on Cepheids, RR Lyrae, and eclipsing binaries,
examining the influence of observational cadence and phase distribution on
classification precision. Employing AutoDL optimization, we achieve striking
performance with the 1D-Convolution+BiLSTM architecture and the Swin
Transformer, hitting accuracies of 94\% and 99\% correspondingly, with the
latter demonstrating a notable 83\% accuracy in discerning the elusive Type II
Cepheids-comprising merely 0.02\% of the total dataset.We unveil StarWhisper
LightCurve (LC), an innovative Series comprising three LLM-based models: LLM,
multimodal large language model (MLLM), and Large Audio Language Model (LALM).
Each model is fine-tuned with strategic prompt engineering and customized
training methods to explore the emergent abilities of these models for
astronomical data. Remarkably, StarWhisper LC Series exhibit high accuracies
around 90\%, significantly reducing the need for explicit feature engineering,
thereby paving the way for streamlined parallel data processing and the
progression of multifaceted multimodal models in astronomical applications. The
study furnishes two detailed catalogs illustrating the impacts of phase and
sampling intervals on deep learning classification accuracy, showing that a
substantial decrease of up to 14\% in observation duration and 21\% in sampling
points can be realized without compromising accuracy by more than 10\%.
