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🔗 Source: arxiv.org
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📄 Pages: 10 pages
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Researchers propose JPQ, a method that jointly optimizes query encoding and Product Quantization to improve retrieval performance in dense retrieval models, addressing the issue of compromised ranking performance due to separate encoding and compression steps. JPQ trains the query encoder and PQ index jointly using ranking-oriented loss, PQ centroid optimization, and end-to-end negative sampling, resulting in significant improvements over popular vector compression methods. Experimental results show JPQ almost matches the best retrieval performance with 30x compression on index size, and the compressed index brings 10x speedup on CPU and 2x speedup on GPU in query latency.

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Researchers propose JPQ, a method that jointly optimizes query encoding and Product Quantization to improve retrieval performance in dense retrieval models,...

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  • File Format: PDF
  • File Size: 4.02 MB
  • Pages: 10
  • Language: EN
  • Total Downloads: 33
  • Last Updated: 2 months ago

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