Improving Text Embeddings with Large Language Models: Training

This paper, authored by researchers at Microsoft, explores a novel method for multilingual information retrieval. The approach involves generating synthetic data to augment training. A contrastive pre-training method is employed for model development. The paper details the synthetic data generation process. The training methodology and experimental setup are thoroughly described. Results demonstrate the effectiveness of the proposed method. Multilingual retrieval performance is analyzed. An investigation into the necessity of contrastive pre-training is included. The study also examines long text embeddings and hyperparameter analysis. The paper concludes with a discussion of findings and future directions.