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Microsoft: Improving Text Embeddings with Large Language Models
Microsoft: Improving Text Embeddings with Large Language Models
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- uses an LLM instead of complex pipelines to create the  training data
- uses an LLM instead of complex pipelines to create the  training data
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- directly generates data for numerous text embedding tasks
- directly generates data for numerous text embedding tasks
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- fine tunes standard models with contrastative loss achieving great performance 
- fine tunes standard models with contrastative loss achieving great performance 
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- critical thought: isn't this kinda benchmark hacking? If the benchmarks are so encompassing that they capture the complete idea of embedding, it's maybe a good idea, but often it is oversimplifying, I find. 
- critical thought: isn't this kinda benchmark hacking? If the benchmarks are so encompassing that they capture the complete idea of embedding, it's maybe a good idea, but often it is oversimplifying, I find. 
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Feel free to share your thoughts, even if they like mine don't beat the benchmarks ;P
Feel free to share your thoughts, even if they like mine don't beat the benchmarks ;P
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https://arxiv.org/abs/2401.00368
https://arxiv.org/abs/2401.00368
Microsoft: Improving Text Embeddings with Large Language Models

- uses an LLM instead of complex pipelines to create the  training data
- directly generates data for numerous text embedding tasks
- fine tunes standard models with contrastative loss achieving great performance 
- critical thought: isn't this kinda benchmark hacking? If the benchmarks are so encompassing that they capture the complete idea of embedding, it's maybe a good idea, but often it is oversimplifying, I find. 

Feel free to share your thoughts, even if they like mine don't beat the benchmarks ;P


https://arxiv.org/abs/2401.00368
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osanseviero pcuenq julien-c alvarobartt nouamanetazi mlninad dvilasuero Praise2112 merve sbrandeis
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Simon Brandeis
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Alvaro Bartolome
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