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ReaderScaling Laws for Sparse Retrieval

Scaling Laws for Sparse Retrieval

Chen, Okafor & Iyer · 2023

We find that retrieval quality improves smoothly with index size, but the gains flatten once the corpus exceeds roughly ten million passages. Beyond that point, better query rewriting matters more than a larger index. This pattern held across all three benchmarks we tested, and across both dense and sparse retrievers.

CanvasRetrieval review

Extract

Beyond that point, better query rewriting matters more than a larger index.

Chen et al. 2023, p. 7

Note

Does this hold for domain-specific corpora? Check the Okafor follow-up.

Reference

Scaling Laws for Sparse Retrieval

Chen, Okafor, Iyer · 2023

Illustrative sample. A highlight in the reader becomes a card on the canvas, linked to its source.

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Retrieval gains flatten beyond ten million passages (Chen et al., 2023), though query rewriting keeps improving results (Okafor, 2024).

References

Chen, L., Okafor, A., & Iyer, R. (2023). Scaling laws for sparse retrieval. Sample Journal, 14(2), 101–118.

Okafor, A. (2024). Query rewriting at scale. Sample Proceedings, 55–63.

Insert citation

@chen

Chen, Okafor & Iyer · 2023

Scaling laws for sparse retrieval

Locator: p. 7

Style: APA 7th

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Illustrative sample.

How Notebarn compares.

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Feature	Notebarn	Zotero	Notion	Heptabase
Cite while write in Word	Native	Plugin only	No	No
PDF reader and highlights	Built in	Good	No	Basic
Highlights to canvas cards	One click	No	No	Manual
Databases and relations	Full	No	Great	Basic
Infinite canvas with nesting	Core	No	No	Great
Offline, full read and write	SQLite + Yjs	Desktop only	Read-only	Read-only
AI over your library	5 structured workflows	No	Chat only	No
Citation graph explorer	Interactive	No	No	No
File format	Open (SQLite + JSON)	Proprietary	Proprietary	Proprietary
Price per month	₹799	Free	~₹1,500	~₹2,000

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