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

Neural Search Talks — Zeta Alpha

We discuss the Information Retrieval publication "The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes" by Nils Reimers and Iryna Gurevych, which explores how Dense Passage Retrieval performance degrades as the index size varies and how it compares to traditional sparse or keyword-based methods.

Timestamps:

00:00 Co-host introduction

00:26 Paper introduction

02:18 Dense vs. Sparse retrieval

05:46 Theoretical analysis of false positives(1)

08:17 What is low vs. high dimensional representations

11:49 Theoretical analysis o false positives (2)

20:10 First results: growing the MS-Marco index

28:35 Adding random strings to the index

39:17 Discussion, takeaways

44:26 Will dense retrieval replace or coexist with sparse methods?

50:50 Sparse, Dense and Attentional Representations for Text Retrieval

Referenced work:

Sparse, Dense and Attentional Representations for Text Retrieval by Yi Luan et al. 2020.