# Data Augmentation for Sample Efficient and Robust Document Ranking

arXiv: https://arxiv.org/abs/2311.15426 · HTML: https://mlsanity.com/paper/2311.15426/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2023.

## Built on by (most cited first, 4 of 4)

- [Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective](https://mlsanity.com/paper/2407.06992/) (2024)
- [Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs](https://mlsanity.com/paper/2406.03614/) (2024)
- [Contrastive Federated Learning with Tabular Data Silos](https://mlsanity.com/paper/2409.06123/) (2024)
- [The Surprising Effectiveness of Rankers Trained on Expanded Queries](https://mlsanity.com/paper/2404.02587/) (2024)

## Abstract (arXiv)

Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine-tuning. In this paper, we propose data-augmentation methods for effective and robust ranking performance. One of the key benefits of using data augmentation is in achieving sample efficiency or learning effectively when we have only a small amount of training data. We propose supervised and unsupervised data augmentation schemes by creating training data using parts of the relevant documents in the query-document pairs. We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model. Our extensive experiments on subsets of the MS MARCO and TREC-DL test sets show that data augmentation, along with the ranking-adapted contrastive losses, results in performance improvements under most dataset sizes. Apart from sample efficiency, we conclusively show that data augmentation results in robust models when transferred to out-of-domain benchmarks. Our performance improvements in in-domain and more prominently in out-of-domain benchmarks show that augmentation regularizes the ranking model and improves its robustness and generalization capability.