Summary of Nv-embed: Improved Techniques For Training Llms As Generalist Embedding Models, by Chankyu Lee et al.
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
by Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 NV-Embed model utilizes an LLM-based architecture to enhance text embedding performance, leveraging architectural designs, training procedures, and curated datasets. The latent attention layer improves retrieval and downstream task accuracy, while removing the causal attention mask during contrastive training boosts representation learning. A two-stage contrastive instruction-tuning method is introduced, featuring in-batch negatives and hard negative examples, which enhances both retrieval and non-retrieval tasks. The model achieves top performance on the MTEB leaderboard across 56 tasks and high scores on the AIR Benchmark’s Long Doc and QA sections. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new text embedding model called NV-Embed that uses LLMs to improve performance in various tasks. The model has some special features like a latent attention layer and removing the causal attention mask, which helps it learn better representations of text. The authors also share two different training methods and use a combination of datasets to train their model. As a result, NV-Embed outperforms other models on several benchmarks. |
Keywords
» Artificial intelligence » Attention » Embedding » Instruction tuning » Mask » Representation learning
