title: Reducing the Footprint of Multi-Vector Retrieval with Minimal
    Performance Impact via Token Pooling
summary: Over the last few years, multi-vector retrieval methods, spearheaded by
  ColBERT, have become an increasingly popular approach to Neural IR. By storing
  representations at the token level rather than at the document level, these
  methods have demonstrated very strong retrieval performance, especially in
  out-of-domain settings. However, the storage and memory requirements necessary
  to store the large number of associated vectors remain an important drawback,
  hindering practical adoption. In this paper, we introduce a simple
  clustering-based token pooling approach to aggressively reduce the number of
  vectors that need to be stored. This method can reduce the space & memory
  footprint of ColBERT indexes by 50% with virtually no retrieval performance
  degradation. This method also allows for further reductions, reducing the
  vector count by 66%-to-75% , with degradation remaining below 5% on a vast
  majority of datasets. Importantly, this approach requires no architectural
  change nor query-time processing, and can be used as a simple drop-in during
  indexation with any ColBERT-like model.
paper_authors._id: 66f64f2b8f96964e4b024e82
paper_authors.name: Benjamin Clavié
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-27T22:20:59.550Z
paper_authors._id: 5ff60d4352c26e9bc240badd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/5ff60d4352c26e9bc240badd/HzoknJibrSasc1ZzU71XA.png
paper_authors.fullname: Benjamin Clavié
paper_authors.type: user
paper_authors.user: bclavie
paper_authors._id: 66f64f2b8f96964e4b024e83
paper_authors.name: Antoine Chaffin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-27T13:48:00.209Z
paper_authors._id: 609bbe2f4932693ca2009d6a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1620819560688-609bbe2f4932693ca2009d6a.jpeg
paper_authors.fullname: Antoine Chaffin
paper_authors.type: user
paper_authors.user: NohTow
paper_authors._id: 66f64f2b8f96964e4b024e84
paper_authors.name: Griffin Adams
paper_title: Reducing the Footprint of Multi-Vector Retrieval with Minimal
    Performance Impact via Token Pooling
paper_summary: Over the last few years, multi-vector retrieval methods, spearheaded by
  ColBERT, have become an increasingly popular approach to Neural IR. By storing
  representations at the token level rather than at the document level, these
  methods have demonstrated very strong retrieval performance, especially in
  out-of-domain settings. However, the storage and memory requirements necessary
  to store the large number of associated vectors remain an important drawback,
  hindering practical adoption. In this paper, we introduce a simple
  clustering-based token pooling approach to aggressively reduce the number of
  vectors that need to be stored. This method can reduce the space & memory
  footprint of ColBERT indexes by 50% with virtually no retrieval performance
  degradation. This method also allows for further reductions, reducing the
  vector count by 66%-to-75% , with degradation remaining below 5% on a vast
  majority of datasets. Importantly, this approach requires no architectural
  change nor query-time processing, and can be used as a simple drop-in during
  indexation with any ColBERT-like model.
paper_ai_summary: A clustering-based token pooling method significantly reduces the storage and memory requirements of ColBERT indexes without compromising retrieval performance.
paper_ai_keywords: multi-vector retrieval ColBERT token level document level retrieval performance vector pooling clustering-based pooling ColBERT indexes query-time processing