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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 9·5 min read
Matryoshka Hash Representations for Model-Aware Compact Semantic Retrieval
TL;DR
Matryoshka Hash Representations (MHR) introduces a novel two-stage quantization method for efficient semantic retrieval, achieving superior performance in low-bit representation scenarios. It outperforms existing methods, reaching .5561 NDCG@10 and .6535 Recall@100 at 32 bytes.
✦ Why It Matters
Implement MHR to improve your semantic retrieval systems, especially under tight storage constraints.
Key Takeaways
- Explore MHR for efficient document storage in retrieval systems.
- Utilize prefix organization to enhance query performance.
- Consider MHR for low-bit representation challenges.
- Integrate MHR with existing pipelines for improved candidate selection.
How It Works
MHR employs a two-stage quantization process. First, it learns a longer binary code for documents, which is then frozen.
Next, it trains additional zero-initialized residual code adaptors that create shorter, directly searchable prefixes. This separation allows for better optimization of both full-width and prefix codes, enhancing retrieval efficiency.