NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

• URL: http://arxiv.org/abs/2405.17428v3
• Date: Tue, 25 Feb 2025 00:35:18 GMT
• Title: NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
• Authors: Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping,
• Abstract summary: We introduce NV-Embed, incorporating architectural designs, training procedures, and curated datasets.<n>For model architecture, we propose a latent attention layer to obtain pooled embeddings.<n>For training algorithm, we introduce a two-stage contrastive instruction-tuning method.
• Score: 38.41524186248607
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decoder-only LLM-based embedding models are beginning to outperform BERT or T5-based embedding models in general-purpose text embedding tasks, including dense vector-based retrieval. In this work, we introduce NV-Embed, incorporating architectural designs, training procedures, and curated datasets to significantly enhance the performance of LLM as a versatile embedding model, while maintaining its simplicity and reproducibility. For model architecture, we propose a latent attention layer to obtain pooled embeddings, which consistently improves retrieval and downstream task accuracy compared to mean pooling or using the last <EOS> token embedding from LLMs. To enhance representation learning, we remove the causal attention mask of LLMs during contrastive training. For training algorithm, we introduce a two-stage contrastive instruction-tuning method. It first applies contrastive training with instructions on retrieval datasets, utilizing in-batch negatives and curated hard negative examples. At stage-2, it blends various non-retrieval into instruction tuning, which not only enhances non-retrieval task accuracy but also improves retrieval performance. For training data, we utilize the hard-negative mining, synthetic data generation and existing public available datasets to boost the performance of embedding model. By combining these techniques, our NV-Embed-v1 and NV-Embed-v2 models obtained the No.1 position on the MTEB leaderboard (as of May 24 and August 30, 2024, respectively) across 56 tasks, demonstrating the sustained effectiveness of the proposed methods over time. It also achieved the highest scores in the Long Doc section and the second-highest scores in the QA section of the AIR Benchmark, which covers a range of out-of-domain information retrieval topics beyond those in MTEB. We further provide the analysis of model compression techniques for generalist embedding models.

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