GM DC Task

Generative modeling under data constraints (GM-DC) focuses on creating models that generate new data similar to training data, even with limited examples. Current research emphasizes developing models capable of few-shot and zero-shot learning, often leveraging transformer-based architectures like Decision Transformers and incorporating multimodal information via Large Language Models (LLMs). This area is crucial for applications where data acquisition is difficult, such as healthcare, and advancements in GM-DC are driving progress in various fields by enabling efficient and generalizable model training with limited resources.