Impact of Denoising Diffusion Processes on Zero-Shot Instruction Following Accuracy in Multimodal Models

We present SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings. First, for stronger vision-language alignment, we unfreeze the large language model (LLM) during pre-training, and introduce a weight mix strategy between LLMs trained by real-world and synthetic data. By directly integrating the weights from two domains, the mixed LLM can efficiently incorporate diverse semantics with favorable robustness. Then, to enable multi-purpose capabilities, we mix a variety of tasks for joint visual instruction tuning, and

Research goal: How does replacing GAN-based layout priors with denoising diffusion processes impact the zero-shot instruction following accuracy of multimodal models on the RefCOCO+ visual grounding benchmark?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.8/10.