Papers › Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder

Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder

6 May 2022arXiv:2205.03284archive 2025-07-28

Zhenghao Liu, Han Zhang, Chenyan Xiong, Zhiyuan Liu, Yu Gu, Xiaohua LI

Dense retrievers encode queries and documents and map them in an embedding space using pre-trained language models. These embeddings need to be high-dimensional to fit training signals and guarantee the retrieval effectiveness of dense retrievers. However, these high-dimensional embeddings lead to larger index storage and higher retrieval latency. To reduce the embedding dimensions of dense retrieval, this paper proposes a Conditional Autoencoder (ConAE) to compress the high-dimensional embeddings to maintain the same embedding distribution and better recover the ranking features. Our experiments show that ConAE is effective in compressing embeddings by achieving comparable ranking performance with its teacher model and making the retrieval system more efficient. Our further analyses show that ConAE can alleviate the redundancy of the embeddings of dense retrieval with only one linear layer. All codes of this work are available at https://github.com/NEUIR/ConAE.

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Tasks

Dimensionality ReductionInformation RetrievalRetrieval

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Information Retrieval MS MARCO ConAE-128 Time (ms) 0.3245 #1 of 3 Archive leaderboard report
Information Retrieval MS MARCO ConAE-256 Time (ms) 0.3294 #2 of 3 Archive leaderboard report

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