Jinhyuk Lee and his team present Gecko, a versatile text embedding model offering robust retrieval performance through distillation from large language models. Gecko utilizes synthetic paired data and refines quality through an LLM for highly effective text embeddings.

Main insights:

• Gecko’s two-step distillation process produces diverse data conducive to retrieval tasks.
• With 256 dimensions, Gecko outperforms competitors with larger embedding sizes on the Massive Text Embedding Benchmark (MTEB).
• Gecko is highly competitive, even against larger models.

Gecko’s innovation contributes to the efficiency and accuracy of text-based information retrieval. Its performance with lower-dimensional embeddings indicates substantial progress in developing practical and effective text embeddings, opening new possibilities for various retrieval and natural language processing tasks.