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

arXiv: https://arxiv.org/abs/2410.20301 · HTML: https://mlsanity.com/paper/2410.20301/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 27 Oct 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 4 of 4)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Grale: Designing Networks for Graph Learning](https://mlsanity.com/paper/2007.12002/) (2020)

## Abstract (arXiv)

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.