Zero Shot Baseline

Zero-shot baselines represent the performance of large pre-trained models applied to new tasks without any further training. Current research focuses on improving these baselines by incorporating techniques like few-shot prompting, style matching, and knowledge augmentation, often leveraging the capabilities of large language models (LLMs) and vision-language models (VLMs) like CLIP. These efforts aim to bridge the performance gap between zero-shot and few-shot learning, leading to more efficient and adaptable AI systems across diverse applications such as machine translation, image reconstruction, and question answering. The ultimate goal is to create robust, generalizable models that require minimal or no task-specific training data.