In this paper, we introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. Unlike existing methods that often depend on multi-stage intermediate pre-training with billions of weakly-supervised text pairs, followed by fine-tuning with a few labeled datasets, our method does not require building complex training pipelines or relying on manually collected datasets that are often constrained by task diversity and language coverage. We leverage proprietary LLMs to generate diverse synthetic data for hundreds of thousands of text embedding tasks across nearly 100 languages. We then fine-tune open-source decoder-only LLMs on the synthetic data using standard contrastive loss. Experiments demonstrate that our method achieves strong performance on highly competitive text embedding benchmarks without using any labeled data. Furthermore, when fine-tuned with a mixture of synthetic and labeled data, our model sets new state-of-the-art results on the BEIR and MTEB benchmarks.

翻译：本文提出了一种新颖且简单的方法，仅使用合成数据和不到1k训练步骤即可获得高质量的文本嵌入。与现有方法通常依赖数十亿弱监督文本对的多阶段中间预训练，随后使用少量标注数据集进行微调不同，我们的方法无需构建复杂的训练流程，也不依赖于通常受任务多样性和语言覆盖范围限制的人工收集数据集。我们利用专有LLMs为近100种语言的数十万个文本嵌入任务生成多样化的合成数据，然后使用标准对比损失对这些合成数据上的开源解码器型LLMs进行微调。实验表明，我们的方法在不使用任何标注数据的情况下，在极具竞争力的文本嵌入基准测试中取得了优异表现。此外，当使用合成数据和标注数据混合进行微调时，我们的模型在BEIR和MTEB基准测试中创下了新的最先进成果。