Analysis of cross-modal understanding and generative capabilities
The multimodal engine of GLM-4.5 makes it one of the few open-source big models that can process both text and images. In terms of technical implementation, the model adopts a dual-encoder architecture: the text branch is based on autoregressive Transformer, and the visual branch uses an improved ViT model, which realizes information fusion through a cross-modal attention mechanism. Its multimodal capabilities are manifested in three dimensions: first, graphic Q&A, such as parsing the picture of a math problem and giving the steps to solve it; second, content generation, outputting a structured report based on the textual description and automatically matching the illustrations; and third, document comprehension, supporting semantic parsing of PDF/PPT and other formats.
In practice, the model achieves 78.2% accuracy on the TextVQA benchmark test, significantly better than open source models of the same parameter size. In terms of commercial applications, the capability is particularly suitable for intelligent customer service (automatic parsing of product diagrams), education technology (graphical solution of math problems), content auditing (graphical consistency checking) and other scenarios. It is worth noting that the current version does not support video processing for the time being, which is one of the main gaps between it and the top closed-source models.
This answer comes from the articleGLM-4.5: Open Source Multimodal Large Model Supporting Intelligent Reasoning and Code GenerationThe
