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Found 4 papers, 0 papers with code
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation
Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent.
Leveraging LLMs for Unsupervised Dense Retriever Ranking
Existing methodologies for ranking dense retrievers fall short in addressing these domain shift scenarios.
Selecting which Dense Retriever to use for Zero-Shot Search
We propose the new problem of choosing which dense retrieval model to use when searching on a new collection for which no labels are available, i. e. in a zero-shot setting.
Rethinking Persistent Homology for Visual Recognition
This paper performs a detailed analysis of the effectiveness of topological properties for image classification in various training scenarios, defined by: the number of training samples, the complexity of the training data and the complexity of the backbone network.
