  Space-based gravitational wave (GW) detection is one of the most anticipated
GW detection projects in the next decade, which promises to detect abundant
compact binary systems. At present, deep learning methods have not been widely
explored for GW waveform generation and extrapolation. To solve the data
processing difficulty and the increasing waveform complexity caused by the
detector's response and second-generation time-delay interferometry (TDI 2.0),
an interpretable pre-trained large model named CBS-GPT (Compact Binary Systems
Waveform Generation with Generative Pre-trained Transformer) is proposed. For
compact binary system waveforms, three models were trained to predict the
waveforms of massive black hole binaries (MBHB), extreme mass-ratio inspirals
(EMRIs), and galactic binaries (GB), achieving prediction accuracies of at most
99%, 91%, and 99%, respectively. The CBS-GPT model exhibits notable
generalization and interpretability, with its hidden parameters effectively
capturing the intricate information of waveforms, even with the complex
instrument response and a wide parameter range. Our research demonstrates the
potential of large models in the GW realm, opening up new opportunities and
guidance for future researches such as complex waveforms generation, gap
completion, and deep learning model design for GW science.
