Improving Text Embeddings with Large Language Models (2024)

Abstract: 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 93 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.

## conclusion this paper shows that the quality of text embeddings can be substantially enhanced by exploiting llms. we prompt proprietary llms such as gpt-4 to generate diverse synthetic data with instructions in many languages. combined with the strong language understanding capability of the mistral model, we establish new state-of-the-art results for nearly all task categories on the competitive mteb benchmark. the training process is much more streamlined and efficient than existing multi-stage approaches, thereby obviating the need for intermediate pre-training. for future work, we aim to further improve the multilingual performance of our model and explore the possibility of using open-source llms to generate synthetic data.