Improving Text Embeddings with Large Language Models: Conclusion and References | HackerNoon
Briefly

The paper demonstrates that utilizing large language models (LLMs) like GPT-4 can significantly enhance the quality of text embeddings through the generation of diverse synthetic data across various languages.
By combining the strong language understanding of the Mistral model with the synthetic data from proprietary LLMs, we achieved state-of-the-art results across nearly all task categories on the MTEB benchmark.
Our streamlined training approach surpasses multi-stage methodologies by removing the need for intermediate pre-training, simplifying the overall process for enhancing multilingual capabilities.
Future work will focus on improving performance in multilingual contexts, exploring open-source LLMs for synthetic data generation, and investigating ways to enhance inference efficiency and reduce storage costs.
Read at Hackernoon
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