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AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings

arXiv 2405.15028 ↗First posted 23 May 2024cs.CL cs.IRcited by 0

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Abstract (arXiv)

Ranking is a fundamental and popular problem in search. However, existing ranking algorithms usually restrict the granularity of ranking to full passages or require a specific dense index for each desired level of granularity. Such lack of flexibility in granularity negatively affects many applications that can benefit from more granular ranking, such as sentence-level ranking for open-domain question-answering, or proposition-level ranking for attribution. In this work, we introduce the idea of any-granularity ranking, which leverages multi-vector embeddings to rank at varying levels of granularity while maintaining encoding at a single (coarser) level of granularity. We propose a multi-granular contrastive loss for training multi-vector approaches, and validate its utility with both sentences and propositions as ranking units. Finally, we demonstrate the application of proposition-level ranking to post-hoc citation addition in retrieval-augmented generation, surpassing the performance of prompt-driven citation generation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 51 references that are in the map. First posted on arXiv 23 May 2024.

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32 of 51 references.

References · 32

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20 more
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  20. 2023Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representationscited by 19

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  5. 2025Embedding-Based Context-Aware Reranker
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  8. 2026Candidate-Constrained Retrieval-Augmented Generation for LongEval-RAG: System Design and Empirical Analysis