Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes? (2025.acl-industry)
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| Challenge: | Currently, practitioners working on dense retrieval face a bewildering number of choices. |
| Approach: | They propose a framework for thinking about retrieval in terms of nearest-neighbor search over vector representations where these representations can be dense (typically called embeddings, generated from transformers) or flat (with brute-force search) |
| Outcome: | The proposed model explicates tradeoffs between HNSW and flat indexes from the perspectives of indexing time, query evaluation performance, and retrieval quality. |
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