Summary of Matryoshka-adaptor: Unsupervised and Supervised Tuning For Smaller Embedding Dimensions, by Jinsung Yoon et al.
Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions
by Jinsung Yoon, Raj Sinha, Sercan O Arik, Tomas Pfister
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 proposed Matryoshka-Adaptor framework is designed to customize embeddings from Large Language Models (LLMs) for improved information retrieval. While high-dimensional embeddings excel in tasks like search, their use is often hindered by increased computational latency and cost. Matryoshka-Adaptor addresses these challenges by reducing dimensionality while maintaining performance levels, achieving a significant boost in efficiency and cost-effectiveness. The framework modifies pre-trained LLM embeddings to be seamlessly integrated with any architecture, including those accessed through black-box APIs. Evaluation across diverse English, multilingual, and multimodal datasets consistently reveals gains with Matryoshka-Adaptor, particularly when using Google and OpenAI Embedding APIs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Matryoshka-Adaptor is a new way to make language models better for searching and retrieving information. It helps reduce the complexity of these models while keeping them just as good at finding what we’re looking for. This makes it faster and cheaper to use. The creators of Matryoshka-Adaptor tested it on lots of different types of data, like English texts, other languages, and even images, and saw that it really works. |
Keywords
* Artificial intelligence * Embedding
