DPR Retrieval Performance Decline with Reduced Context Window Sizes on SQuAD 2.0

Abstract

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

Research Question

How does DPR retrieval performance (Recall@5) on SQuAD 2.0 vary when context window size is reduced from 4096 to 1024 tokens?

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