Space-based gravitational wave detection is one of the most anticipated
gravitational wave (GW) detection projects in the next decade, which will
detect abundant compact binary systems. However, the precise prediction of
space GW waveforms remains unexplored. To solve the data processing difficulty
in the increasing waveform complexity caused by detectors' 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 binary (MBHB), extreme mass-ratio inspirals (EMRIs), and
galactic binary (GB), achieving prediction accuracies of 98%, 91%, and 99%,
respectively. The CBS-GPT model exhibits notable interpretability, with its
hidden parameters effectively capturing the intricate information of waveforms,
even with complex instrument response and a wide parameter range. Our research
demonstrates the potential of large pre-trained models in gravitational wave
data processing, opening up new opportunities for future tasks such as gap
completion, GW signal detection, and signal noise reduction