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
Recent articles
- The last six months in LLMs, illustrated by pelicans on bicycles - 6th June 2025
- Tips on prompting ChatGPT for UK technology secretary Peter Kyle - 3rd June 2025
- How often do LLMs snitch? Recreating Theo's SnitchBench with LLM - 31st May 2025