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The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes
Manage episode 355037190 series 3446693
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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.
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