NVIDIA’s NV-Embed: Superior Performance in Embedding Tasks Without Proprietary Data

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Published in
3 min readMay 29, 2024

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For years, embedding models based on bidirectional language models have led the field, excelling in retrieval and general-purpose embedding tasks. However, past top-tier methods have relied on fine-tuning Large Language Models (LLMs) with extensive amounts of proprietary synthetic data from GPT-4, which isn’t accessible to the broader community.

In a new paper NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models, an NVIDIA research team introduces NV-Embed. This generalist embedding model significantly boosts the performance of decoder-only LLMs in embedding and retrieval tasks while maintaining simplicity and reproducibility.

The team presents a novel latent attention layer for their model architecture, which pools embeddings from a sequence of tokens. Unlike the commonly used average pooling in bidirectional embedding models or the last token embedding in decoder-only LLMs, this new pooling method consistently…

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