WindTunnel -- A Framework for Community Aware Sampling of Large Corpora

TL;DR

WindTunnel is a framework that creates representative samples of large datasets by preserving community structures, enabling more efficient and accurate information retrieval experiments in big data and neural retrieval contexts.

Contribution

It introduces a novel sampling method that maintains community structures, improving the accuracy of retrieval evaluations on large corpora.

Findings

Reduces computational costs of retrieval experiments

Provides more representative samples of large datasets

Enhances evaluation accuracy in neural retrieval

Abstract

Conducting comprehensive information retrieval experiments, such as in search or retrieval augmented generation, often comes with high computational costs. This is because evaluating a retrieval algorithm requires indexing the entire corpus, which is significantly larger than the set of (query, result) pairs under evaluation. This issue is especially pronounced in big data and neural retrieval, where indexing becomes increasingly time-consuming and complex. In this paper, we present WindTunnel, a novel framework developed at Yext to generate representative samples of large corpora, enabling efficient end-to-end information retrieval experiments. By preserving the community structure of the dataset, WindTunnel overcomes limitations in current sampling methods, providing more accurate evaluations.