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Paper explores dimensionality limits in retrieval models

By PulseAugur Editorial · [2 sources] ·

Researchers have investigated why low-dimensional representations, typically around 1000 dimensions, do not hinder the scalability of modern embedding-based retrieval models to trillions of data points. Their study focuses on maximal-margin embeddings, establishing that a near-optimal margin can be achieved with a dimension dependent on the logarithm of the data size. The findings resolve a previous setup concerning k-sparse rows and suggest that sigmoid loss outperforms InfoNCE for generating large-margin embeddings. AI

IMPACT This research provides theoretical insights into the scalability of retrieval models, potentially influencing future model design for large-scale AI applications.

RANK_REASON The cluster contains an academic paper published on arXiv discussing theoretical and empirical aspects of machine learning models.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Paper explores dimensionality limits in retrieval models

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Kiril Bangachev, Guy Bresler, Jonathan Kogan, Yury Polyanskiy ·

    Is Dimensionality a Barrier for Retrieval Models?

    arXiv:2605.23556v1 Announce Type: new Abstract: Why does the low dimensionality of representations, typically $d\approx 1000$, not prevent modern embedding-based retrieval models from scaling to billions, or even trillions, of data points? To answer this question, we study maxima…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Yury Polyanskiy ·

    Is Dimensionality a Barrier for Retrieval Models?

    Why does the low dimensionality of representations, typically $d\approx 1000$, not prevent modern embedding-based retrieval models from scaling to billions, or even trillions, of data points? To answer this question, we study maximal-margin embeddings in the following retrieval m…

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