The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes

Nils Reimers, Iryna Gurevych


Abstract
Information Retrieval using dense low-dimensional representations recently became popular and showed out-performance to traditional sparse-representations like BM25. However, no previous work investigated how dense representations perform with large index sizes. We show theoretically and empirically that the performance for dense representations decreases quicker than sparse representations for increasing index sizes. In extreme cases, this can even lead to a tipping point where at a certain index size sparse representations outperform dense representations. We show that this behavior is tightly connected to the number of dimensions of the representations: The lower the dimension, the higher the chance for false positives, i.e. returning irrelevant documents
Anthology ID:
2021.acl-short.77
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
605–611
Language:
URL:
https://aclanthology.org/2021.acl-short.77
DOI:
10.18653/v1/2021.acl-short.77
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-short.77.pdf