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    <abstract>Dense Passage Retrieval (DPR) typically relies on Euclidean or cosine distance to measure query–passage relevance in embedding space, which is effective when embeddings lie on a linear manifold. However, our experiments across DPR benchmarks suggest that embeddings often lie on lower-dimensional, non-linear manifolds, especially in out-of-distribution (OOD) settings, where cosine and Euclidean distance fail to capture semantic similarity. To address this limitation, we propose a *manifold-aware* distance metric for DPR (**MA-DPR**) that models the intrinsic manifold structure of passages using a nearest-neighbor graph and measures query–passage distance based on their shortest path in this graph. We show that MA-DPR outperforms Euclidean and cosine distances by up to **26%** on OOD passage retrieval, with comparable in-distribution performance across various embedding models, while incurring a minimal increase in query inference time. Empirical evidence suggests that manifold-aware distance allows DPR to leverage context from related neighboring passages, making it effective even in the absence of direct semantic overlap. MA-DPR can be applied to a wide range of dense embedding and retrieval tasks, offering potential benefits across a wide spectrum of domains.</abstract>
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%0 Conference Proceedings
%T MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval
%A Liu, Yifan
%A Wen, Qianfeng
%A Zhao, Mark
%A Liang, Jiazhou
%A Sanner, Scott
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F liu-etal-2025-ma-dpr
%X Dense Passage Retrieval (DPR) typically relies on Euclidean or cosine distance to measure query–passage relevance in embedding space, which is effective when embeddings lie on a linear manifold. However, our experiments across DPR benchmarks suggest that embeddings often lie on lower-dimensional, non-linear manifolds, especially in out-of-distribution (OOD) settings, where cosine and Euclidean distance fail to capture semantic similarity. To address this limitation, we propose a *manifold-aware* distance metric for DPR (**MA-DPR**) that models the intrinsic manifold structure of passages using a nearest-neighbor graph and measures query–passage distance based on their shortest path in this graph. We show that MA-DPR outperforms Euclidean and cosine distances by up to **26%** on OOD passage retrieval, with comparable in-distribution performance across various embedding models, while incurring a minimal increase in query inference time. Empirical evidence suggests that manifold-aware distance allows DPR to leverage context from related neighboring passages, making it effective even in the absence of direct semantic overlap. MA-DPR can be applied to a wide range of dense embedding and retrieval tasks, offering potential benefits across a wide spectrum of domains.
%R 10.18653/v1/2025.emnlp-main.1582
%U https://aclanthology.org/2025.emnlp-main.1582/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1582
%P 31085-31103

Markdown (Informal)

[MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval](https://aclanthology.org/2025.emnlp-main.1582/) (Liu et al., EMNLP 2025)

• MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval (Liu et al., EMNLP 2025)

ACL

• Yifan Liu, Qianfeng Wen, Mark Zhao, Jiazhou Liang, and Scott Sanner. 2025. MA-DPR: Manifold-aware Distance Metrics for Dense Passage Retrieval. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 31085–31103, Suzhou, China. Association for Computational Linguistics.