Improving Text Embeddings with Large Language Models: Conclusion and References

Table of Links

Abstract and 1 Introduction

2 Related Work

3 Method

3.1 Synthetic Data Generation

3.2 Training

4 Experiments

4.1 Statistics of the Synthetic Data

4.2 Model Fine-tuning and Evaluation

4.3 Main Results

4.4 Multilingual Retrieval

5 Analysis

5.1 Is Contrastive Pre-training Necessary?

5.2 Extending to Long Text Embeddings and 5.3 Analysis of Training Hyperparameters

6 Conclusion and References

A Implementation Details

B Test Set Contamination Analysis

C Prompts for Synthetic Data Generation

D Instructions for Training and Evaluation

6 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. We also intend to investigate ways to improve the inference efficiency and lower the storage cost for LLM based text embeddings.

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