Imagine an AI that can effortlessly transform messy, unstructured data into perfectly organized, structured formats like JSON. That's the promise of Structured Object Language Modeling (SoLM), a novel approach to training Large Language Models (LLMs) that's turning heads in the AI community. Traditional LLMs often struggle to consistently output structured data, requiring complex prompt engineering and multiple API calls. SoLM tackles this challenge head-on by using a self-supervised denoising method. Think of it like training an LLM to solve a complex jigsaw puzzle, where the pieces are bits of data, and the final picture is a complete, structured object. This innovative training process teaches SoLM to reconstruct structured data from a corrupted version, learning the intricate relationships between different data facets. This allows SoLM to not only generate new structured objects from unstructured text blurbs or image captions, but also clean, complete, and correct existing structured data. The results are impressive. In tests on e-commerce product data, SoLM matched the performance of heavily prompt-engineered state-of-the-art models like Claude 3.0, but at a fraction of the computational cost. In online A/B testing, product titles generated by SoLM actually boosted sales and engagement. While SoLM shines in generating and regenerating structured data, it still has room to grow in capturing nuanced human preferences. Future research will explore incorporating techniques like Reinforcement Learning from Human Feedback (RLHF) to further refine SoLM's abilities. This research opens exciting possibilities for automating data processing, improving data quality, and building more efficient AI systems that seamlessly interact with structured information. From streamlining e-commerce product listings to organizing complex databases, SoLM's ability to unlock the power of structured data has the potential to revolutionize how we interact with information.