Simon Willison’s Weblog

10th October 2024

Bridging Language Gaps in Multilingual Embeddings via Contrastive Learning (via) Most text embeddings models suffer from a "language gap", where phrases in different languages with the same semantic meaning end up with embedding vectors that aren't clustered together.

Jina claim their new jina-embeddings-v3 (CC BY-NC 4.0, which means you need to license it for commercial use if you're not using their API) is much better on this front, thanks to a training technique called "contrastive learning".

There are 30 languages represented in our contrastive learning dataset, but 97% of pairs and triplets are in just one language, with only 3% involving cross-language pairs or triplets. But this 3% is enough to produce a dramatic result: Embeddings show very little language clustering and semantically similar texts produce close embeddings regardless of their language