Improving Text Embeddings with Large Language Models

This paper introduces a novel method for enhancing text embeddings by leveraging large language models and synthetic data.

The approach involves generating diverse synthetic data and fine-tuning open-source language models to achieve high-quality text embeddings without relying on labeled data.

The method demonstrates strong performance on competitive benchmarks and sets new state-of-the-art results when combined with labeled data.