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：在本文中，我们提出了一种新颖且简单的方法，仅使用合成数据和不到1000步训练即可获得高质量的文本嵌入。与现有方法通常依赖多阶段中间预训练（涉及数十亿弱监督文本对）再通过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或依赖手动收集、受限于任务多样性和语言覆盖范围的数据集。我们利用专有的大型语言模型在近100种语言中生成数十万项文本嵌入任务的多样化合成数据，随后使用标准对比损失对开源解码器专用大型语言模型进行微调。实验表明，该方法在极具竞争力的文本嵌入基准测试中无需使用任何标注数据即可取得优异表现。此外，当使用合成数据和标注数据的混合数据进行微调时，我们的模型在BEIR和MTEB基准测试中创造了新的最佳结果。