Papers with situated retrieval capabilities

1 papers
Situated Embedding Models for Context-Aware Dense Retrieval (2026.acl-short)

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Challenge: Existing embedding models are not well-equipped to encode situated context effectively, i.e., situating a chunk’s meaning within its context.
Approach: They propose to represent short chunks in a way that is conditioned on a broader context window to enhance retrieval performance.
Outcome: The proposed model outperforms state-of-the-art embedding models on a book-plot retrieval dataset.

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