1 citations · 2 across the 3 of their papers we have counts for
3 papers
Source-Free Domain-Invariant Performance Prediction
Ekaterina Khramtsova, Mahsa Baktashmotlagh, Guido Zuccon +2
Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most exist…
Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection
Ekaterina Khramtsova, Teerapong Leelanupab, Shengyao Zhuang +2
In this demo we present a web-based application for selecting an effective pre-trained dense retriever to use on a private collection. Our system, DenseQuest, provides unsupervised…
FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation
Shuai Wang, Ekaterina Khramtsova, Shengyao Zhuang +1
Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent. With the increasing upt…