Shot Indexing

Shot indexing focuses on efficiently indexing and retrieving information from large datasets using only a limited number of labeled examples ("few-shot learning"). Current research emphasizes leveraging large language models (LLMs) and exploring novel indexing methods, such as prompt-based generation of identifiers, to overcome the limitations of traditional training-based approaches. This area is significant because it addresses the high computational cost and data requirements of many machine learning tasks, potentially improving the efficiency and scalability of various applications, including information retrieval and satellite image analysis. The development of metrics to assess the inherent difficulty of few-shot learning for different datasets is also a key area of investigation.