MEMORY-VQ: Compression for Tractable Internet-Scale Memory

• URL: http://arxiv.org/abs/2308.14903v1
• Date: Mon, 28 Aug 2023 21:11:18 GMT
• Title: MEMORY-VQ: Compression for Tractable Internet-Scale Memory
• Authors: Yury Zemlyanskiy, Michiel de Jong, Luke Vilnis, Santiago Onta\~n\'on, William W. Cohen, Sumit Sanghai, Joshua Ainslie
• Abstract summary: Memory-based methods like LUMEN pre-compute token representations for retrieved passages to drastically speed up inference. We propose MEMORY-VQ, a new method to reduce storage requirements of memory-augmented models without sacrificing performance.
• Score: 45.7528997281282
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval augmentation is a powerful but expensive method to make language models more knowledgeable about the world. Memory-based methods like LUMEN pre-compute token representations for retrieved passages to drastically speed up inference. However, memory also leads to much greater storage requirements from storing pre-computed representations. We propose MEMORY-VQ, a new method to reduce storage requirements of memory-augmented models without sacrificing performance. Our method uses a vector quantization variational autoencoder (VQ-VAE) to compress token representations. We apply MEMORY-VQ to the LUMEN model to obtain LUMEN-VQ, a memory model that achieves a 16x compression rate with comparable performance on the KILT benchmark. LUMEN-VQ enables practical retrieval augmentation even for extremely large retrieval corpora.

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