Better Zero

"Better Zero" research focuses on improving the performance of machine learning models in zero-shot and few-shot learning scenarios, minimizing the need for large, labeled training datasets. Current efforts concentrate on developing novel prompt engineering techniques, leveraging pre-trained large language models (LLMs) and vision-language models (VLMs), and designing efficient algorithms for proxy search and model adaptation. This research is significant because it addresses the limitations of data-hungry models, potentially enabling wider application of AI in resource-constrained domains and accelerating the development of more generalizable AI systems.