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Summary of Nudge: Lightweight Non-parametric Fine-tuning Of Embeddings For Retrieval, by Sepanta Zeighami et al.


NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval

by Sepanta Zeighami, Zac Wellmer, Aditya Parameswaran

First submitted to arxiv on: 4 Sep 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The paper presents a novel approach to fine-tuning dense vector embeddings for k-Nearest Neighbor (k-NN) retrieval in text and image datasets. Existing methods either fine-tune the pre-trained model or train adapter models, but these approaches have limitations. NUDGE is a family of non-parametric embedding fine-tuning methods that directly modify data record embeddings to maximize k-NN accuracy. The authors provide a theoretical and experimental study on NUDGE’s non-parametric approach, showing it can efficiently solve constrained variations while avoiding semantic distortions. In experiments across five pre-trained models and nine datasets, NUDGE improves NDCG@10 by over 10% compared to existing fine-tuning methods, running in minutes and offering significant accuracy increases.
Low GrooveSquid.com (original content) Low Difficulty Summary
The paper is about a new way to improve how computers find the most similar text or images. Right now, people often adjust pre-trained computer models to make them better at finding what they’re looking for. But this can be time-consuming and doesn’t always work well. The researchers developed a new approach called NUDGE that makes adjustments directly to the data records themselves. This helps computers find more accurate results faster. They tested NUDGE on many different datasets and found it improved accuracy by over 10% compared to other methods, while also being much faster.

Keywords

» Artificial intelligence  » Embedding  » Fine tuning  » Nearest neighbor