Research on the Application Algorithm of Multimodal Learning in GPT Large Model

This paper discusses the research on the application algorithm of multimodal learning based on the GPT model. Combined with the Transformer architecture, a cross-modal alignment algorithm is proposed to optimize the deep fusion of text and visual information. By introducing the generative adversarial network (GAN), the generation and conversion capabilities of multimodal data are further improved, and efficient collaboration between different modalities is achieved. In the system design, the multimodal data is first preprocessed, and then feature extraction and information aggregation are performed through the encoder and decoder structure based on the Transformer. Then, the cross-modal alignment algorithm is used to match and convert the features of different modal data. In the final stage, the performance of the architecture is improved by strengthening the adversarial generative network, aiming to enhance the system’s performance in dealing with complex cross-modal tasks. This study selected a series of public databases for testing. The actual test results show that the proposed technology has shown significant improvements in topics such as image text creation and image category judgment. For the image-to-text synthesis task, the matching rate between the output text and the calibrated image increased by 15.7%. For the image category classification task, the judgment accuracy increased by 12.4%. The experimental evidence has conclusively proved that this text technology has substantial effects in the field of cross-media learning, opening a new perspective for the subsequent in-depth exploration of cross-media learning principles.