Improving Text Embeddings with Large Language Models
Authors: name: Liang Wang, name: Nan Yang, name: Xiaolong Huang, name: Linjun Yang, name: Rangan Majumder, name: Furu Wei

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.