# Improving Dense Passage Retrieval with Multiple Positive Passages

arXiv: https://arxiv.org/abs/2508.09534 · HTML: https://mlsanity.com/paper/2508.09534/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [Inferential Question Answering](https://mlsanity.com/paper/2602.01239/) (2026)

## Abstract (arXiv)

By leveraging a dual encoder architecture, Dense Passage Retrieval (DPR) has outperformed traditional sparse retrieval algorithms such as BM25 in terms of passage retrieval accuracy. Recently proposed methods have further enhanced DPR's performance. However, these models typically pair each question with only one positive passage during training, and the effect of associating multiple positive passages has not been examined. In this paper, we explore the performance of DPR when additional positive passages are incorporated during training. Experimental results show that equipping each question with multiple positive passages consistently improves retrieval accuracy, even when using a significantly smaller batch size, which enables training on a single GPU.