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Summary of Repurposing Language Models Into Embedding Models: Finding the Compute-optimal Recipe, by Alicja Ziarko et al.


Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe

by Alicja Ziarko, Albert Q. Jiang, Bartosz Piotrowski, Wenda Li, Mateja Jamnik, Piotr Miłoś

First submitted to arxiv on: 6 Jun 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: None

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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
This paper presents a novel approach to training text embeddings in a computationally efficient manner, utilizing pre-trained decoder-only language models as the foundation. The authors propose an algorithm that optimizes model size, data quantity, and fine-tuning methods for various computational budget levels. Through extensive experimentation, they derive a recipe for practitioners to make informed design choices for their embedding models. The results indicate that full fine-tuning is optimal at lower budgets, while low-rank adaptation fine-tuning is superior at higher budgets.
Low GrooveSquid.com (original content) Low Difficulty Summary
This paper helps us create better text embeddings. Text embeddings are like special codes that help computers understand what words mean. They’re useful for lots of things like searching for documents or grouping similar texts together. The researchers in this paper found a way to make these text embeddings quickly and efficiently using pre-trained language models. They came up with an algorithm that helps people choose the right settings for their embedding models, depending on how much computer power they have. This is important because it can help us create more accurate and efficient text embeddings.

Keywords

» Artificial intelligence  » Decoder  » Embedding  » Fine tuning  » Low rank adaptation