Image-Text Pairs: The Fuel for Multi-modal Large Language Models 🖼️✍️

Image-Text Pairs: Building the Foundation for Multi-modal AI 🖼️✍️ In the era of Multi-modal Large Language Models (like CLIP, BLIP, and LLaVA), Image-Text Pairs are the most critical data assets. Whether it's pre-training, fine-tuning, or evaluation, the quality of your image-text alignment directly determines the model's ability to "see" and "describe." Based on the data_engineering_book , this post breaks down how to construct, validate, and pipe multi-modal data for production-grade AI. 1. What are Image-Text Pairs? An image-text pair consists of one image and one or more matching textual descriptions. The core requirement is Strong Semantic Alignment . Core Scenarios Scenario Data Requirement Image-Text Retrieval Precise descriptions of core features, zero redundancy. V-L Pre-training Massive diversity (People, Landscapes, Goods) and varied styles. Generative AI (Stable Diffusion) Rich detail (Colors, Textures, Actions) corresponding to every pixel. 2. Building High-Quality Dataset